{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX C MODELS.12-07-2024.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Dec 2024, 11:51:16
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. *** MODELS PREDICTING VARIOPUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
. 
. 
. 
. 
. 
. *** MODEL 1: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***
. 
. *  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *
. 
. 
. 
. *** MODEL 2: ABSOLUTE TYPE I ERROR RATE ***
. 
. * [overpayment error rate / paid claims sample] *
. 
. 
. 
. 
. *** MODEL 3: RELATIVE TYPE I ERROR RATE:  {c -(}TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]{c )-}      (SAMPLE WEIGHTED)  ***
. 
. *  {c -(}[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]{c )-}  *
. 
. 
. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** APPENDIX C MODELS: ANALYZING SENSITIVITY OF MANUSCRIPT MODEL ESTIMATES -- INCLUSION OF NON-IT MODERNIZATION STATES DURING PRE-IT MODERNIZATION REFORM ADOPTION PHASE ***
. 
. 
. 
. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** RETRIEVE MANUSCRIPT MODELS DATABASE [as of 12-06-2024] ***
. 
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.12-06-2024.dta", replace
{txt}
{com}. 
. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
. 
. 
. **** TABLE C1 -- MODELS C1-C3: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" APPENDIX C STATISTICAL ANALYSES [DECEMBER 2024]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [TOTAL PROGRAM ERROR RATE] **** 
. 
. 
. ** (MODEL C1; FIGURES C1A-C1C; MODEL C2: FIGURES C2A-C2C; MODEL C3: FIGURES C2D-C2F) **
. 
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS] ***
. 
. ** PURPOSE: COMPUTE MARGINAL DIFFERENTIAL EFFECTS IN MANUSCRIPT MODELS [BASED ON EFFECTIVE SAMPLE OF OBSERVATIONS] **
. 
. 
. 
. ** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS 1 & 3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL 2] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt 
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}     .025              0       {txt}Obs         {res}     11,935
{txt}25%    {res} .0527778              0       {txt}Sum of wgt. {res}     11,935

{txt}50%    {res}       .1                      {txt}Mean          {res} .1277776
                        {txt}Largest       Std. dev.     {res} .1178167
{txt}75%    {res} .1666667       1.172269
{txt}90%    {res} .2592593       1.178571       {txt}Variance      {res} .0138808
{txt}95%    {res} .3333333       1.202381       {txt}Skewness      {res} 2.517165
{txt}99%    {res} .5833334       1.240385       {txt}Kurtosis      {res}  13.7885
{txt}
{com}. di r(p75)
{res}.16666667
{txt}
{com}. di r(p25)
{res}.05277778
{txt}
{com}. *
. gen tot_interstate_catC =.
{txt}(12,551 missing values generated)

{com}. replace tot_interstate_catC = 0 if tot_interstate<= r(p25) 
{txt}(3,012 real changes made)

{com}. replace tot_interstate_catC = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,853 real changes made)

{com}. replace tot_interstate_catC = 2 if tot_interstate>= r(p75) 
{txt}(3,686 real changes made)

{com}. *
. tab tot_interstate_catC

{txt}tot_interst {c |}
   ate_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,012       24.00       24.00
{txt}          1 {c |}{res}      5,853       46.63       70.63
{txt}          2 {c |}{res}      3,686       29.37      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab tot_interstate_catC if itmod_adopt_state==1

{txt}tot_interst {c |}
   ate_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,134       28.89       28.89
{txt}          1 {c |}{res}      3,534       47.85       76.74
{txt}          2 {c |}{res}      1,718       23.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt
{txt}
{com}. *
. *
. sum t1_interstate if e(sample), detail

                        {txt}t1_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}     12,355
{txt}25%    {res} .0222222              0       {txt}Sum of wgt. {res}     12,355

{txt}50%    {res}      .05                      {txt}Mean          {res} .0636324
                        {txt}Largest       Std. dev.     {res} .0671194
{txt}75%    {res} .0857143       .6153846
{txt}90%    {res} .1428571       .6428571       {txt}Variance      {res}  .004505
{txt}95%    {res} .1891892       .6785714       {txt}Skewness      {res} 2.327178
{txt}99%    {res}      .32       .7857143       {txt}Kurtosis      {res} 12.41854
{txt}
{com}. di r(p75)
{res}.08571429
{txt}
{com}. di r(p25)
{res}.02222222
{txt}
{com}. *
. gen t1_interstate_catC =.
{txt}(12,551 missing values generated)

{com}. replace t1_interstate_catC = 0 if t1_interstate<= r(p25) 
{txt}(3,170 real changes made)

{com}. replace t1_interstate_catC = 1 if t1_interstate> r(p25) & t1_interstate < r(p75) 
{txt}(6,056 real changes made)

{com}. replace t1_interstate_catC = 2 if t1_interstate>= r(p75) 
{txt}(3,325 real changes made)

{com}. *
. tab t1_interstate_catC

{txt}t1_intersta {c |}
    te_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,170       25.26       25.26
{txt}          1 {c |}{res}      6,056       48.25       73.51
{txt}          2 {c |}{res}      3,325       26.49      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab t1_interstate_catC if itmod_adopt_state==1

{txt}t1_intersta {c |}
    te_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,181       29.53       29.53
{txt}          1 {c |}{res}      3,652       49.44       78.97
{txt}          2 {c |}{res}      1,553       21.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}     .025              0       {txt}Obs         {res}     11,589
{txt}25%    {res} .0527778              0       {txt}Sum of wgt. {res}     11,589

{txt}50%    {res}       .1                      {txt}Mean          {res} .1272231
                        {txt}Largest       Std. dev.     {res} .1163632
{txt}75%    {res} .1666667       1.115079
{txt}90%    {res} .2571693       1.172269       {txt}Variance      {res} .0135404
{txt}95%    {res} .3333333       1.178571       {txt}Skewness      {res} 2.472013
{txt}99%    {res} .5807927       1.202381       {txt}Kurtosis      {res}  13.4281
{txt}
{com}. di r(p75)
{res}.16666667
{txt}
{com}. di r(p25)
{res}.05277778
{txt}
{com}. *
. gen relt1_interstate_catC =.
{txt}(12,551 missing values generated)

{com}. replace relt1_interstate_catC = 0 if tot_interstate<= r(p25) 
{txt}(3,012 real changes made)

{com}. replace relt1_interstate_catC = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,853 real changes made)

{com}. replace relt1_interstate_catC = 2 if tot_interstate>= r(p75) 
{txt}(3,686 real changes made)

{com}. *
. tab relt1_interstate_catC

{txt}relt1_inter {c |}
 state_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,012       24.00       24.00
{txt}          1 {c |}{res}      5,853       46.63       70.63
{txt}          2 {c |}{res}      3,686       29.37      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab relt1_interstate_catC if itmod_adopt_state==1

{txt}relt1_inter {c |}
 state_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,134       28.89       28.89
{txt}          1 {c |}{res}      3,534       47.85       76.74
{txt}          2 {c |}{res}      1,718       23.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. ** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS 1 & 3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL 2] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt
{txt}
{com}. 
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1309042              0
{txt} 5%    {res}  .212963              0
{txt}10%    {res} .2722536              0       {txt}Obs         {res}     11,935
{txt}25%    {res} .3888889              0       {txt}Sum of wgt. {res}     11,935

{txt}50%    {res} .5294686                      {txt}Mean          {res} .5401146
                        {txt}Largest       Std. dev.     {res} .2113216
{txt}75%    {res} .6783784       1.431944
{txt}90%    {res} .8168498       1.463727       {txt}Variance      {res} .0446568
{txt}95%    {res} .9025974       1.488889       {txt}Skewness      {res} .3724764
{txt}99%    {res} 1.075758       1.543478       {txt}Kurtosis      {res} 3.124776
{txt}
{com}. di r(p75)
{res}.6783784
{txt}
{com}. di r(p25)
{res}.3888889
{txt}
{com}. *
. gen tot_diffoccupseek_catC =.
{txt}(12,551 missing values generated)

{com}. replace tot_diffoccupseek_catC = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,024 real changes made)

{com}. replace tot_diffoccupseek_catC = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,928 real changes made)

{com}. replace tot_diffoccupseek_catC = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,599 real changes made)

{com}. *
. tab tot_diffoccupseek_catC

{txt}tot_diffocc {c |}
upseek_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,024       24.09       24.09
{txt}          1 {c |}{res}      5,928       47.23       71.32
{txt}          2 {c |}{res}      3,599       28.68      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab tot_diffoccupseek_catC if itmod_adopt_state==1

{txt}tot_diffocc {c |}
upseek_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,689       22.87       22.87
{txt}          1 {c |}{res}      3,457       46.80       69.67
{txt}          2 {c |}{res}      2,240       30.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt
{txt}
{com}. *
. *
. sum t1_diffoccupseek if e(sample), detail

                      {txt}t1_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .0357143              0
{txt} 5%    {res} .0833333              0
{txt}10%    {res} .1142857              0       {txt}Obs         {res}     12,355
{txt}25%    {res}     .175              0       {txt}Sum of wgt. {res}     12,355

{txt}50%    {res} .2444444                      {txt}Mean          {res} .2530842
                        {txt}Largest       Std. dev.     {res} .1138628
{txt}75%    {res} .3214286           .875
{txt}90%    {res} .3947369       .8913044       {txt}Variance      {res} .0129647
{txt}95%    {res} .4444444       .8947368       {txt}Skewness      {res} .8435493
{txt}99%    {res} .5714286       .9333333       {txt}Kurtosis      {res} 5.255965
{txt}
{com}. di r(p75)
{res}.32142857
{txt}
{com}. di r(p25)
{res}.175
{txt}
{com}. *
. gen t1_diffoccupseek_catC =.
{txt}(12,551 missing values generated)

{com}. replace t1_diffoccupseek_catC = 0 if t1_diffoccupseek<= r(p25) 
{txt}(3,134 real changes made)

{com}. replace t1_diffoccupseek_catC = 1 if t1_diffoccupseek> r(p25) & t1_diffoccupseek < r(p75) 
{txt}(6,110 real changes made)

{com}. replace t1_diffoccupseek_catC = 2 if t1_diffoccupseek>= r(p75) 
{txt}(3,307 real changes made)

{com}. *
. tab t1_diffoccupseek_catC

{txt}t1_diffoccu {c |}
 pseek_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,134       24.97       24.97
{txt}          1 {c |}{res}      6,110       48.68       73.65
{txt}          2 {c |}{res}      3,307       26.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab t1_diffoccupseek_catC if itmod_adopt_state==1

{txt}t1_diffoccu {c |}
 pseek_catC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,578       21.36       21.36
{txt}          1 {c |}{res}      3,650       49.42       70.78
{txt}          2 {c |}{res}      2,158       29.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year   adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1361111              0
{txt} 5%    {res} .2146572              0
{txt}10%    {res} .2738095       .0181818       {txt}Obs         {res}     11,589
{txt}25%    {res} .3888889       .0222222       {txt}Sum of wgt. {res}     11,589

{txt}50%    {res} .5307692                      {txt}Mean          {res}  .541554
                        {txt}Largest       Std. dev.     {res} .2112089
{txt}75%    {res} .6805556       1.431944
{txt}90%    {res} .8190477       1.463727       {txt}Variance      {res} .0446092
{txt}95%    {res} .9047619       1.488889       {txt}Skewness      {res} .3886722
{txt}99%    {res} 1.078125       1.543478       {txt}Kurtosis      {res} 3.113478
{txt}
{com}. di r(p75)
{res}.68055558
{txt}
{com}. di r(p25)
{res}.3888889
{txt}
{com}. *
. gen relt1_diffoccupseek_catC =.
{txt}(12,551 missing values generated)

{com}. replace relt1_diffoccupseek_catC = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,024 real changes made)

{com}. replace relt1_diffoccupseek_catC = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,952 real changes made)

{com}. replace relt1_diffoccupseek_catC = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,575 real changes made)

{com}. *
. tab relt1_diffoccupseek_catC

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
         tC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,024       24.09       24.09
{txt}          1 {c |}{res}      5,952       47.42       71.52
{txt}          2 {c |}{res}      3,575       28.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab relt1_diffoccupseek_catC if itmod_adopt_state==1

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
         tC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,689       22.87       22.87
{txt}          1 {c |}{res}      3,471       46.99       69.86
{txt}          2 {c |}{res}      2,226       30.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H1 & H3: TOTAL/OVERALL PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ***
. 
. 
. 
. 
. *** ESTIMATE MODEL C1: TOTAL PROGRAM ERROR RATE [MODEL 1 with ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERROIRS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***     (FIGURES C1A-C1C) 
. 
. 
. npregress series totalerror_rat  itmod_monthcount i.tot_interstate_catC  i.tot_diffoccupseek_catC, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat    i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}510{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}520{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}530{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}540{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}550{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}560{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}570{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}580{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}590{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}600{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}610{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}620{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}630{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}640{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}650{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}660{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}670{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}680{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}690{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}700{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}710{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}720{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}730{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}740{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}750{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}760{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}770{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}780{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}790{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}800{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}810{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}820{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}830{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}840{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}850{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}860{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}870{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}880{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}890{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}900{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}910{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}920{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}930{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}940{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}950{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}960{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}970{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}980{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}990{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}1,000{text} done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:npregress}.
{res}
{txt}Cubic B-spline estimation {col 44}Number of obs      =  {res}       11,936
{txt}Criterion: {res:cross validation}{col 44}Number of knots    =  {res}            1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}totalerror~t{col 14}{c |}     effect{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
itmod_mon~nt {c |}{col 14}{res}{space 2}-.0002098{col 26}{space 2} .0002648{col 37}{space 1}   -0.79{col 46}{space 3}0.428{col 54}{space 4}-.0007288{col 67}{space 3} .0003092
{txt}{space 12} {c |}
tot_inters~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0026646{col 26}{space 2} .0024447{col 37}{space 1}   -1.09{col 46}{space 3}0.276{col 54}{space 4}-.0074562{col 67}{space 3} .0021269
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0047035{col 26}{space 2} .0034614{col 37}{space 1}   -1.36{col 46}{space 3}0.174{col 54}{space 4}-.0114877{col 67}{space 3} .0020808
{txt}{space 12} {c |}
tot_diffoc~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0112669{col 26}{space 2} .0024213{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4} .0065212{col 67}{space 3} .0160125
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .018777{col 26}{space 2} .0031025{col 37}{space 1}    6.05{col 46}{space 3}0.000{col 54}{space 4} .0126963{col 67}{space 3} .0248577
{txt}{space 12} {c |}
{space 1}demgovparty {c |}{col 14}{res}{space 2} .0475879{col 26}{space 2} .0076827{col 37}{space 1}    6.19{col 46}{space 3}0.000{col 54}{space 4} .0325301{col 67}{space 3} .0626457
{txt}{space 1}repgovparty {c |}{col 14}{res}{space 2} .0424661{col 26}{space 2} .0074752{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4}  .027815{col 67}{space 3} .0571171
{txt}{space 1}ln_workload {c |}{col 14}{res}{space 2} .0020326{col 26}{space 2} .0025944{col 37}{space 1}    0.78{col 46}{space 3}0.433{col 54}{space 4}-.0030523{col 67}{space 3} .0071175
{txt}automation~e {c |}{col 14}{res}{space 2} .0471299{col 26}{space 2} .0059268{col 37}{space 1}    7.95{col 46}{space 3}0.000{col 54}{space 4} .0355136{col 67}{space 3} .0587462
{txt}ln_uiadmin~l {c |}{col 14}{res}{space 2} .0512353{col 26}{space 2}  .008932{col 37}{space 1}    5.74{col 46}{space 3}0.000{col 54}{space 4} .0337289{col 67}{space 3} .0687417
{txt}benefitgen~2 {c |}{col 14}{res}{space 2} .0495667{col 26}{space 2} .0153193{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0195414{col 67}{space 3}  .079592
{txt}{space 2}unemp_rate {c |}{col 14}{res}{space 2} .0042197{col 26}{space 2} .0010726{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .0021174{col 67}{space 3} .0063221
{txt}ln_functio~l {c |}{col 14}{res}{space 2}-.0197461{col 26}{space 2} .0075457{col 37}{space 1}   -2.62{col 46}{space 3}0.009{col 54}{space 4}-.0345354{col 67}{space 3}-.0049568
{txt}tot_totaln~t {c |}{col 14}{res}{space 2} .0202401{col 26}{space 2}  .006337{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .0078198{col 67}{space 3} .0326603
{txt}tot_totalf~t {c |}{col 14}{res}{space 2} .0145019{col 26}{space 2} .0067203{col 37}{space 1}    2.16{col 46}{space 3}0.031{col 54}{space 4} .0013304{col 67}{space 3} .0276734
{txt}tot_totala~t {c |}{col 14}{res}{space 2} .0101128{col 26}{space 2} .0096129{col 37}{space 1}    1.05{col 46}{space 3}0.293{col 54}{space 4}-.0087281{col 67}{space 3} .0289537
{txt}{space 12} {c |}
{space 5}stateid {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  .126293{col 26}{space 2} .0114005{col 37}{space 1}   11.08{col 46}{space 3}0.000{col 54}{space 4} .1039484{col 67}{space 3} .1486376
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0446681{col 26}{space 2} .0087183{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .0275805{col 67}{space 3} .0617557
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .072547{col 26}{space 2} .0098749{col 37}{space 1}    7.35{col 46}{space 3}0.000{col 54}{space 4} .0531926{col 67}{space 3} .0919015
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0824378{col 26}{space 2} .0246161{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.1306845{col 67}{space 3}-.0341912
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0465363{col 26}{space 2} .0110098{col 37}{space 1}    4.23{col 46}{space 3}0.000{col 54}{space 4} .0249575{col 67}{space 3} .0681151
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0451397{col 26}{space 2} .0097133{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4}  .026102{col 67}{space 3} .0641774
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0911999{col 26}{space 2} .0150686{col 37}{space 1}    6.05{col 46}{space 3}0.000{col 54}{space 4} .0616659{col 67}{space 3} .1207338
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0522697{col 26}{space 2} .0130448{col 37}{space 1}   -4.01{col 46}{space 3}0.000{col 54}{space 4}-.0778371{col 67}{space 3}-.0267023
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0416146{col 26}{space 2} .0109871{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .0200802{col 67}{space 3} .0631489
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1441443{col 26}{space 2} .0108557{col 37}{space 1}   13.28{col 46}{space 3}0.000{col 54}{space 4} .1228676{col 67}{space 3} .1654211
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0304229{col 26}{space 2}  .011185{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .0085008{col 67}{space 3} .0523451
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .037403{col 26}{space 2} .0160298{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0059852{col 67}{space 3} .0688207
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2266844{col 26}{space 2} .0199024{col 37}{space 1}   11.39{col 46}{space 3}0.000{col 54}{space 4} .1876765{col 67}{space 3} .2656923
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .1615274{col 26}{space 2} .0097787{col 37}{space 1}   16.52{col 46}{space 3}0.000{col 54}{space 4} .1423614{col 67}{space 3} .1806934
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .1147551{col 26}{space 2} .0119312{col 37}{space 1}    9.62{col 46}{space 3}0.000{col 54}{space 4} .0913705{col 67}{space 3} .1381398
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .0264912{col 26}{space 2} .0098224{col 37}{space 1}    2.70{col 46}{space 3}0.007{col 54}{space 4} .0072397{col 67}{space 3} .0457426
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .2143555{col 26}{space 2} .0149281{col 37}{space 1}   14.36{col 46}{space 3}0.000{col 54}{space 4} .1850969{col 67}{space 3} .2436141
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .1997133{col 26}{space 2} .0122524{col 37}{space 1}   16.30{col 46}{space 3}0.000{col 54}{space 4} .1756989{col 67}{space 3} .2237277
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0260786{col 26}{space 2} .0103599{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0057736{col 67}{space 3} .0463836
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .1334107{col 26}{space 2} .0114167{col 37}{space 1}   11.69{col 46}{space 3}0.000{col 54}{space 4} .1110344{col 67}{space 3}  .155787
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.0493793{col 26}{space 2} .0152427{col 37}{space 1}   -3.24{col 46}{space 3}0.001{col 54}{space 4}-.0792544{col 67}{space 3}-.0195043
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .0551526{col 26}{space 2} .0117775{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4} .0320691{col 67}{space 3}  .078236
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.0868402{col 26}{space 2} .0095061{col 37}{space 1}   -9.14{col 46}{space 3}0.000{col 54}{space 4}-.1054717{col 67}{space 3}-.0682086
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .0009317{col 26}{space 2} .0085065{col 37}{space 1}    0.11{col 46}{space 3}0.913{col 54}{space 4}-.0157407{col 67}{space 3} .0176041
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .1625423{col 26}{space 2} .0126714{col 37}{space 1}   12.83{col 46}{space 3}0.000{col 54}{space 4} .1377069{col 67}{space 3} .1873778
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .0320638{col 26}{space 2} .0075756{col 37}{space 1}    4.23{col 46}{space 3}0.000{col 54}{space 4} .0172159{col 67}{space 3} .0469118
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .1831017{col 26}{space 2} .0137638{col 37}{space 1}   13.30{col 46}{space 3}0.000{col 54}{space 4} .1561251{col 67}{space 3} .2100782
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.0129474{col 26}{space 2}  .014059{col 37}{space 1}   -0.92{col 46}{space 3}0.357{col 54}{space 4}-.0405025{col 67}{space 3} .0146076
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0676111{col 26}{space 2} .0263672{col 37}{space 1}    2.56{col 46}{space 3}0.010{col 54}{space 4} .0159322{col 67}{space 3} .1192899
{txt}{space 9}32  {c |}{col 14}{res}{space 2}-.0134133{col 26}{space 2}  .018283{col 37}{space 1}   -0.73{col 46}{space 3}0.463{col 54}{space 4}-.0492473{col 67}{space 3} .0224208
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .0016174{col 26}{space 2} .0097112{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.0174162{col 67}{space 3} .0206509
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .1320055{col 26}{space 2}  .017971{col 37}{space 1}    7.35{col 46}{space 3}0.000{col 54}{space 4}  .096783{col 67}{space 3}  .167228
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .0490854{col 26}{space 2} .0206749{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} .0085633{col 67}{space 3} .0896075
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .0142512{col 26}{space 2} .0076267{col 37}{space 1}    1.87{col 46}{space 3}0.062{col 54}{space 4}-.0006969{col 67}{space 3} .0291993
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .0408282{col 26}{space 2} .0101965{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .0208434{col 67}{space 3} .0608131
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .1647654{col 26}{space 2} .0196958{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .1261624{col 67}{space 3} .2033684
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .1056411{col 26}{space 2}  .011446{col 37}{space 1}    9.23{col 46}{space 3}0.000{col 54}{space 4} .0832074{col 67}{space 3} .1280748
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .0151681{col 26}{space 2} .0090908{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0026494{col 67}{space 3} .0329857
{txt}{space 9}41  {c |}{col 14}{res}{space 2}  .131638{col 26}{space 2} .0174443{col 37}{space 1}    7.55{col 46}{space 3}0.000{col 54}{space 4} .0974477{col 67}{space 3} .1658282
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .1394898{col 26}{space 2} .0083365{col 37}{space 1}   16.73{col 46}{space 3}0.000{col 54}{space 4} .1231505{col 67}{space 3} .1558291
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.0519326{col 26}{space 2} .0163353{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-.0839492{col 67}{space 3}-.0199161
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .0409443{col 26}{space 2} .0116099{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .0181894{col 67}{space 3} .0636993
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1664076{col 26}{space 2} .0190133{col 37}{space 1}    8.75{col 46}{space 3}0.000{col 54}{space 4} .1291423{col 67}{space 3} .2036729
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .0778428{col 26}{space 2} .0097481{col 37}{space 1}    7.99{col 46}{space 3}0.000{col 54}{space 4} .0587369{col 67}{space 3} .0969487
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.0209322{col 26}{space 2}  .012471{col 37}{space 1}   -1.68{col 46}{space 3}0.093{col 54}{space 4}-.0453749{col 67}{space 3} .0035105
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .1582117{col 26}{space 2} .0141517{col 37}{space 1}   11.18{col 46}{space 3}0.000{col 54}{space 4} .1304748{col 67}{space 3} .1859486
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .0125943{col 26}{space 2} .0098242{col 37}{space 1}    1.28{col 46}{space 3}0.200{col 54}{space 4}-.0066609{col 67}{space 3} .0318494
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .1138037{col 26}{space 2} .0167945{col 37}{space 1}    6.78{col 46}{space 3}0.000{col 54}{space 4}  .080887{col 67}{space 3} .1467204
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .1604723{col 26}{space 2} .0124157{col 37}{space 1}   12.92{col 46}{space 3}0.000{col 54}{space 4} .1361381{col 67}{space 3} .1848065
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .1142835{col 26}{space 2} .0113462{col 37}{space 1}   10.07{col 46}{space 3}0.000{col 54}{space 4} .0920453{col 67}{space 3} .1365216
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2003  {c |}{col 14}{res}{space 2} .0035596{col 26}{space 2} .0052098{col 37}{space 1}    0.68{col 46}{space 3}0.494{col 54}{space 4}-.0066513{col 67}{space 3} .0137706
{txt}{space 7}2004  {c |}{col 14}{res}{space 2} .0116228{col 26}{space 2} .0055545{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .0007362{col 67}{space 3} .0225094
{txt}{space 7}2005  {c |}{col 14}{res}{space 2} .0096005{col 26}{space 2} .0059065{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0019761{col 67}{space 3} .0211771
{txt}{space 7}2006  {c |}{col 14}{res}{space 2}   .01407{col 26}{space 2} .0061267{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4}  .002062{col 67}{space 3} .0260781
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} .0049246{col 26}{space 2} .0061078{col 37}{space 1}    0.81{col 46}{space 3}0.420{col 54}{space 4}-.0070465{col 67}{space 3} .0168957
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} .0013645{col 26}{space 2} .0061295{col 37}{space 1}    0.22{col 46}{space 3}0.824{col 54}{space 4} -.010649{col 67}{space 3}  .013378
{txt}{space 7}2009  {c |}{col 14}{res}{space 2}-.0080004{col 26}{space 2} .0071607{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4} -.022035{col 67}{space 3} .0060343
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} .0147732{col 26}{space 2} .0078662{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0006443{col 67}{space 3} .0301907
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} .0044179{col 26}{space 2} .0069317{col 37}{space 1}    0.64{col 46}{space 3}0.524{col 54}{space 4}-.0091681{col 67}{space 3} .0180038
{txt}{space 7}2012  {c |}{col 14}{res}{space 2} -.003466{col 26}{space 2} .0069216{col 37}{space 1}   -0.50{col 46}{space 3}0.617{col 54}{space 4} -.017032{col 67}{space 3}    .0101
{txt}{space 7}2013  {c |}{col 14}{res}{space 2} .0041796{col 26}{space 2} .0072187{col 37}{space 1}    0.58{col 46}{space 3}0.563{col 54}{space 4}-.0099687{col 67}{space 3}  .018328
{txt}{space 7}2014  {c |}{col 14}{res}{space 2} .0099466{col 26}{space 2} .0073316{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.0044232{col 67}{space 3} .0243163
{txt}{space 7}2015  {c |}{col 14}{res}{space 2} .0044141{col 26}{space 2} .0074144{col 37}{space 1}    0.60{col 46}{space 3}0.552{col 54}{space 4}-.0101178{col 67}{space 3}  .018946
{txt}{space 7}2016  {c |}{col 14}{res}{space 2} .0096217{col 26}{space 2} .0073126{col 37}{space 1}    1.32{col 46}{space 3}0.188{col 54}{space 4}-.0047106{col 67}{space 3} .0239541
{txt}{space 7}2017  {c |}{col 14}{res}{space 2} .0194403{col 26}{space 2} .0078348{col 37}{space 1}    2.48{col 46}{space 3}0.013{col 54}{space 4} .0040844{col 67}{space 3} .0347962
{txt}{space 7}2018  {c |}{col 14}{res}{space 2} .0205271{col 26}{space 2} .0086463{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} .0035805{col 67}{space 3} .0374736
{txt}{space 7}2019  {c |}{col 14}{res}{space 2} .0010376{col 26}{space 2} .0078797{col 37}{space 1}    0.13{col 46}{space 3}0.895{col 54}{space 4}-.0144064{col 67}{space 3} .0164815
{txt}{space 7}2020  {c |}{col 14}{res}{space 2}-.0030261{col 26}{space 2} .0102456{col 37}{space 1}   -0.30{col 46}{space 3}0.768{col 54}{space 4}-.0231072{col 67}{space 3}  .017055
{txt}{space 7}2021  {c |}{col 14}{res}{space 2} .1075924{col 26}{space 2} .0117469{col 37}{space 1}    9.16{col 46}{space 3}0.000{col 54}{space 4} .0845688{col 67}{space 3} .1306159
{txt}{space 7}2022  {c |}{col 14}{res}{space 2} .0756453{col 26}{space 2} .0115466{col 37}{space 1}    6.55{col 46}{space 3}0.000{col 54}{space 4} .0530144{col 67}{space 3} .0982762
{txt}{space 12} {c |}
ad~2_itadopt {c |}{col 14}{res}{space 2} .1190901{col 26}{space 2}  .028878{col 37}{space 1}    4.12{col 46}{space 3}0.000{col 54}{space 4} .0624902{col 67}{space 3}   .17569
{txt}a~04_itadopt {c |}{col 14}{res}{space 2} .0221766{col 26}{space 2} .0185715{col 37}{space 1}    1.19{col 46}{space 3}0.232{col 54}{space 4}-.0142229{col 67}{space 3} .0585762
{txt}a~06_itadopt {c |}{col 14}{res}{space 2}-.0038886{col 26}{space 2} .0114334{col 37}{space 1}   -0.34{col 46}{space 3}0.734{col 54}{space 4}-.0262976{col 67}{space 3} .0185203
{txt}a~07_itadopt {c |}{col 14}{res}{space 2}  .022589{col 26}{space 2} .0124895{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0018901{col 67}{space 3}  .047068
{txt}ad~9_itadopt {c |}{col 14}{res}{space 2}  .118974{col 26}{space 2} .0111722{col 37}{space 1}   10.65{col 46}{space 3}0.000{col 54}{space 4} .0970769{col 67}{space 3}  .140871
{txt}a~10_itadopt {c |}{col 14}{res}{space 2} .0628233{col 26}{space 2} .0143378{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .0347218{col 67}{space 3} .0909248
{txt}ad~3_itadopt {c |}{col 14}{res}{space 2} .0367008{col 26}{space 2} .0092787{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .0185149{col 67}{space 3} .0548868
{txt}a~14_itadopt {c |}{col 14}{res}{space 2}-.0966929{col 26}{space 2} .0265549{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4}-.1487395{col 67}{space 3}-.0446462
{txt}ad~5_itadopt {c |}{col 14}{res}{space 2}-.0144041{col 26}{space 2} .0123578{col 37}{space 1}   -1.17{col 46}{space 3}0.244{col 54}{space 4} -.038625{col 67}{space 3} .0098168
{txt}a~16_itadopt {c |}{col 14}{res}{space 2}-.0709423{col 26}{space 2} .0106077{col 37}{space 1}   -6.69{col 46}{space 3}0.000{col 54}{space 4} -.091733{col 67}{space 3}-.0501515
{txt}a~17_itadopt {c |}{col 14}{res}{space 2} .0162884{col 26}{space 2} .0124055{col 37}{space 1}    1.31{col 46}{space 3}0.189{col 54}{space 4}-.0080258{col 67}{space 3} .0406027
{txt}ad~8_itadopt {c |}{col 14}{res}{space 2}-.0510226{col 26}{space 2} .0127217{col 37}{space 1}   -4.01{col 46}{space 3}0.000{col 54}{space 4}-.0759566{col 67}{space 3}-.0260886
{txt}a~20_itadopt {c |}{col 14}{res}{space 2} .1072111{col 26}{space 2} .0222998{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .0635042{col 67}{space 3}  .150918
{txt}ad~1_itadopt {c |}{col 14}{res}{space 2}-.0177477{col 26}{space 2}  .033962{col 37}{space 1}   -0.52{col 46}{space 3}0.601{col 54}{space 4}-.0843121{col 67}{space 3} .0488166
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m1c if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m1c if e(sample), residuals
{res}{txt}(616 missing values generated)

{com}. 
. gen sse_m1c = predsy_m1c * predsy_m1c if e(sample)
{txt}(616 missing values generated)

{com}. gen ssr_m1c = residsy_m1c * residsy_m1c if e(sample)
{txt}(616 missing values generated)

{com}. 
. egen sum_sse_m1c = total(sse_m1c) if e(sample)
{txt}(615 missing values generated)

{com}. egen sum_ssr_m1c = total(ssr_m1c) if e(sample)
{txt}(615 missing values generated)

{com}. 
. gen r2_m1c = sum_ssr_m1c/(sum_sse_m1c + sum_ssr_m1c)
{txt}(615 missing values generated)

{com}. 
. sum r2_m1c

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m1c {c |}{res}     11,936    .2337111           0   .2337111   .2337111
{txt}
{com}. 
. 
. 
. 
. * [MODEL C1: TOTAL PROGRAM ERROR RATE] FIGURE C1A: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:11,935}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1669045{col 26}{space 2} .0018322{col 37}{space 1}   91.10{col 46}{space 3}0.000{col 54}{space 4} .1633136{col 67}{space 3} .1704955
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .166724{col 26}{space 2} .0015931{col 37}{space 1}  104.65{col 46}{space 3}0.000{col 54}{space 4} .1636016{col 67}{space 3} .1698464
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1658048{col 26}{space 2}    .0012{col 37}{space 1}  138.18{col 46}{space 3}0.000{col 54}{space 4} .1634529{col 67}{space 3} .1681567
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1646632{col 26}{space 2} .0022482{col 37}{space 1}   73.24{col 46}{space 3}0.000{col 54}{space 4} .1602567{col 67}{space 3} .1690697
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1634763{col 26}{space 2} .0034541{col 37}{space 1}   47.33{col 46}{space 3}0.000{col 54}{space 4} .1567065{col 67}{space 3} .1702461
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1622407{col 26}{space 2} .0045118{col 37}{space 1}   35.96{col 46}{space 3}0.000{col 54}{space 4} .1533977{col 67}{space 3} .1710836
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1609528{col 26}{space 2} .0053941{col 37}{space 1}   29.84{col 46}{space 3}0.000{col 54}{space 4} .1503806{col 67}{space 3} .1715249
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1596092{col 26}{space 2} .0061081{col 37}{space 1}   26.13{col 46}{space 3}0.000{col 54}{space 4} .1476376{col 67}{space 3} .1715809
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1582065{col 26}{space 2} .0066693{col 37}{space 1}   23.72{col 46}{space 3}0.000{col 54}{space 4}  .145135{col 67}{space 3} .1712781
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1567413{col 26}{space 2} .0070957{col 37}{space 1}   22.09{col 46}{space 3}0.000{col 54}{space 4} .1428339{col 67}{space 3} .1706487
{txt}{space 9}11  {c |}{col 14}{res}{space 2}   .15521{col 26}{space 2} .0074067{col 37}{space 1}   20.96{col 46}{space 3}0.000{col 54}{space 4} .1406931{col 67}{space 3} .1697269
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1536092{col 26}{space 2} .0076221{col 37}{space 1}   20.15{col 46}{space 3}0.000{col 54}{space 4} .1386702{col 67}{space 3} .1685482
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE C1A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Total Program Error Rate [MODEL C1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1A.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1A.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1A.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. 
. * [MODEL C1: TOTAL PROGRAM ERROR RATE] FIGURE C1B: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_cat==2) & LOW COMPLEXITY (tot_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_interstate_catC if tot_interstate_catC==0|tot_interstate_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,082}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 24}{c TT}{hline 11}{hline 12}{hline 11}
{col 25}{text}{c |}         df{col 37}        chi2{col 49}     P>chi2
{res}{col 1}{text}{hline 24}{c +}{hline 11}{hline 12}{hline 11}
tot_interstate_catC@_at {c |}
{space 11}(2 vs 0)  1  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.03{col 49}{space 2}   0.8644
{txt}{space 11}(2 vs 0)  2  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.00{col 49}{space 2}   0.9930
{txt}{space 11}(2 vs 0)  3  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.36{col 49}{space 2}   0.5509
{txt}{space 11}(2 vs 0)  4  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.56{col 49}{space 2}   0.4527
{txt}{space 11}(2 vs 0)  5  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.41{col 49}{space 2}   0.5201
{txt}{space 11}(2 vs 0)  6  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.19{col 49}{space 2}   0.6603
{txt}{space 11}(2 vs 0)  7  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.03{col 49}{space 2}   0.8597
{txt}{space 11}(2 vs 0)  8  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.02{col 49}{space 2}   0.8899
{txt}{space 11}(2 vs 0)  9  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.26{col 49}{space 2}   0.6120
{txt}{space 11}(2 vs 0) 10  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     0.87{col 49}{space 2}   0.3517
{txt}{space 11}(2 vs 0) 11  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     1.99{col 49}{space 2}   0.1587
{txt}{space 11}(2 vs 0) 12  {res}{col 25}{text}{c |}{result}{space 2}        1{col 37}{space 3}     3.74{col 49}{space 2}   0.0532
{col 1}{text}                 Joint {col 25}{c |}{result}  (not testable)
{col 1}{text}{hline 24}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 25}{c |}{col 37} Delta-method
{col 25}{c |}   Contrast{col 37}   std. err.{col 49}     [95% con{col 62}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_interstate_catC@_at {c |}
{space 11}(2 vs 0)  1  {c |}{col 25}{res}{space 2}-.0006477{col 37}{space 2} .0037934{col 48}{space 5}-.0080826{col 62}{space 3} .0067873
{txt}{space 11}(2 vs 0)  2  {c |}{col 25}{res}{space 2}-.0000325{col 37}{space 2} .0036958{col 48}{space 5}-.0072761{col 62}{space 3} .0072111
{txt}{space 11}(2 vs 0)  3  {c |}{col 25}{res}{space 2} .0024172{col 37}{space 2}  .004053{col 48}{space 5}-.0055266{col 62}{space 3} .0103611
{txt}{space 11}(2 vs 0)  4  {c |}{col 25}{res}{space 2} .0040522{col 37}{space 2} .0053961{col 48}{space 5} -.006524{col 62}{space 3} .0146284
{txt}{space 11}(2 vs 0)  5  {c |}{col 25}{res}{space 2} .0043766{col 37}{space 2}  .006804{col 48}{space 5}-.0089591{col 62}{space 3} .0177122
{txt}{space 11}(2 vs 0)  6  {c |}{col 25}{res}{space 2} .0035099{col 37}{space 2} .0079865{col 48}{space 5}-.0121433{col 62}{space 3} .0191631
{txt}{space 11}(2 vs 0)  7  {c |}{col 25}{res}{space 2} .0015716{col 37}{space 2} .0088905{col 48}{space 5}-.0158534{col 62}{space 3} .0189965
{txt}{space 11}(2 vs 0)  8  {c |}{col 25}{res}{space 2}-.0013189{col 37}{space 2} .0095296{col 48}{space 5}-.0199967{col 62}{space 3} .0173588
{txt}{space 11}(2 vs 0)  9  {c |}{col 25}{res}{space 2}-.0050422{col 37}{space 2} .0099419{col 48}{space 5} -.024528{col 62}{space 3} .0144437
{txt}{space 11}(2 vs 0) 10  {c |}{col 25}{res}{space 2}-.0094787{col 37}{space 2} .0101773{col 48}{space 5}-.0294258{col 62}{space 3} .0104685
{txt}{space 11}(2 vs 0) 11  {c |}{col 25}{res}{space 2} -.014509{col 37}{space 2} .0102929{col 48}{space 5}-.0346828{col 62}{space 3} .0056648
{txt}{space 11}(2 vs 0) 12  {c |}{col 25}{res}{space 2}-.0200137{col 37}{space 2} .0103502{col 48}{space 5}-.0402996{col 62}{space 3} .0002722
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C1B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Total Program Error Rate [MODEL C1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1B.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1B.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1B.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. 
. * [MODEL C1: TOTAL ERROR RATE] FIGURE C1C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_cat==2) & LOW COMPLEXITY (tot_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_diffoccupseek_catC if tot_diffoccupseek_catC==0|tot_diffoccupseek_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,007}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 27}{c TT}{hline 11}{hline 12}{hline 11}
{col 28}{text}{c |}         df{col 40}        chi2{col 52}     P>chi2
{res}{col 1}{text}{hline 27}{c +}{hline 11}{hline 12}{hline 11}
tot_diffoccupseek_catC@_at {c |}
{space 14}(2 vs 0)  1  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    61.18{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  2  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    62.54{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  3  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    40.06{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  4  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}    16.47{col 52}{space 2}   0.0000
{txt}{space 14}(2 vs 0)  5  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     7.66{col 52}{space 2}   0.0057
{txt}{space 14}(2 vs 0)  6  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     3.94{col 52}{space 2}   0.0473
{txt}{space 14}(2 vs 0)  7  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     2.07{col 52}{space 2}   0.1503
{txt}{space 14}(2 vs 0)  8  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     1.02{col 52}{space 2}   0.3123
{txt}{space 14}(2 vs 0)  9  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.41{col 52}{space 2}   0.5229
{txt}{space 14}(2 vs 0) 10  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.08{col 52}{space 2}   0.7708
{txt}{space 14}(2 vs 0) 11  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.00{col 52}{space 2}   0.9590
{txt}{space 14}(2 vs 0) 12  {res}{col 28}{text}{c |}{result}{space 2}        1{col 40}{space 3}     0.16{col 52}{space 2}   0.6906
{col 1}{text}                    Joint {col 28}{c |}{result}  (not testable)
{col 1}{text}{hline 27}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 28}{c |}{col 40} Delta-method
{col 28}{c |}   Contrast{col 40}   std. err.{col 52}     [95% con{col 65}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_diffoccupseek_catC@_at {c |}
{space 14}(2 vs 0)  1  {c |}{col 28}{res}{space 2} .0257491{col 40}{space 2}  .003292{col 51}{space 5}  .019297{col 65}{space 3} .0322013
{txt}{space 14}(2 vs 0)  2  {c |}{col 28}{res}{space 2}  .025381{col 40}{space 2} .0032095{col 51}{space 5} .0190906{col 65}{space 3} .0316715
{txt}{space 14}(2 vs 0)  3  {c |}{col 28}{res}{space 2} .0234567{col 40}{space 2} .0037062{col 51}{space 5} .0161928{col 65}{space 3} .0307207
{txt}{space 14}(2 vs 0)  4  {c |}{col 28}{res}{space 2} .0209727{col 40}{space 2} .0051684{col 51}{space 5} .0108429{col 65}{space 3} .0311026
{txt}{space 14}(2 vs 0)  5  {c |}{col 28}{res}{space 2} .0183122{col 40}{space 2} .0066185{col 51}{space 5} .0053402{col 65}{space 3} .0312842
{txt}{space 14}(2 vs 0)  6  {c |}{col 28}{res}{space 2} .0154902{col 40}{space 2} .0078087{col 51}{space 5} .0001854{col 65}{space 3}  .030795
{txt}{space 14}(2 vs 0)  7  {c |}{col 28}{res}{space 2} .0125219{col 40}{space 2} .0087044{col 51}{space 5}-.0045385{col 65}{space 3} .0295822
{txt}{space 14}(2 vs 0)  8  {c |}{col 28}{res}{space 2} .0094223{col 40}{space 2} .0093259{col 51}{space 5}-.0088563{col 65}{space 3} .0277008
{txt}{space 14}(2 vs 0)  9  {c |}{col 28}{res}{space 2} .0062064{col 40}{space 2} .0097137{col 51}{space 5}-.0128321{col 65}{space 3}  .025245
{txt}{space 14}(2 vs 0) 10  {c |}{col 28}{res}{space 2} .0028895{col 40}{space 2} .0099187{col 51}{space 5}-.0165507{col 65}{space 3} .0223297
{txt}{space 14}(2 vs 0) 11  {c |}{col 28}{res}{space 2}-.0005135{col 40}{space 2} .0099989{col 51}{space 5}-.0201109{col 65}{space 3} .0190839
{txt}{space 14}(2 vs 0) 12  {c |}{col 28}{res}{space 2}-.0039874{col 40}{space 2} .0100173{col 51}{space 5} -.023621{col 65}{space 3} .0156462
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C1C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Total Program Error Rate [MODEL C1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1C.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1C.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C1.FIGURE C1C.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H2 & H4: ABSOLUTE TYPE I PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ***
. 
. 
. 
. *** ESTIMATE MODEL C2: ABSOLUTE TYPE I PROGRAM ERROR RATE [MODEL 2 with ADDITIONAL COVARIATES: PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***     (FIGURES C2A-C2C) 
. 
. 
. npregress series t1error_rat  itmod_monthcount  i.t1_interstate_catC  i.t1_diffoccupseek_catC, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat    i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}510{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}520{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}530{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}540{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}550{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}560{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}570{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}580{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}590{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}600{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}610{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}620{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}630{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}640{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}650{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}660{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}670{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}680{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}690{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}700{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}710{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}720{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}730{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}740{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}750{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}760{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}770{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}780{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}790{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}800{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}810{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}820{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}830{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}840{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}850{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}860{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}870{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}880{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}890{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}900{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}910{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}920{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}930{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}940{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}950{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}960{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}970{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}980{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}990{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}1,000{text} done
{res}
{txt}Cubic B-spline estimation {col 44}Number of obs      =  {res}       12,355
{txt}Criterion: {res:cross validation}{col 44}Number of knots    =  {res}            1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1} t1error_rat{col 14}{c |}     effect{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
itmod_mon~nt {c |}{col 14}{res}{space 2}-.0004599{col 26}{space 2} .0001444{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-.0007428{col 67}{space 3}-.0001769
{txt}{space 12} {c |}
t1_interst~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0009372{col 26}{space 2} .0015008{col 37}{space 1}    0.62{col 46}{space 3}0.532{col 54}{space 4}-.0020044{col 67}{space 3} .0038788
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0009385{col 26}{space 2} .0021238{col 37}{space 1}    0.44{col 46}{space 3}0.659{col 54}{space 4}-.0032241{col 67}{space 3}  .005101
{txt}{space 12} {c |}
t1_diffocc~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0036993{col 26}{space 2} .0015941{col 37}{space 1}    2.32{col 46}{space 3}0.020{col 54}{space 4} .0005749{col 67}{space 3} .0068237
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0080343{col 26}{space 2} .0019438{col 37}{space 1}    4.13{col 46}{space 3}0.000{col 54}{space 4} .0042245{col 67}{space 3} .0118441
{txt}{space 12} {c |}
{space 1}demgovparty {c |}{col 14}{res}{space 2} .0144004{col 26}{space 2} .0041953{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .0061778{col 67}{space 3} .0226231
{txt}{space 1}repgovparty {c |}{col 14}{res}{space 2} .0129333{col 26}{space 2} .0040177{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .0050587{col 67}{space 3} .0208078
{txt}{space 1}ln_workload {c |}{col 14}{res}{space 2}-.0024038{col 26}{space 2} .0016292{col 37}{space 1}   -1.48{col 46}{space 3}0.140{col 54}{space 4} -.005597{col 67}{space 3} .0007895
{txt}automation~e {c |}{col 14}{res}{space 2} .0256375{col 26}{space 2} .0037305{col 37}{space 1}    6.87{col 46}{space 3}0.000{col 54}{space 4} .0183259{col 67}{space 3} .0329492
{txt}ln_uiadmin~l {c |}{col 14}{res}{space 2} .0222749{col 26}{space 2} .0053894{col 37}{space 1}    4.13{col 46}{space 3}0.000{col 54}{space 4} .0117119{col 67}{space 3} .0328378
{txt}benefitgen~2 {c |}{col 14}{res}{space 2}  .051951{col 26}{space 2} .0106481{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .0310811{col 67}{space 3} .0728209
{txt}{space 2}unemp_rate {c |}{col 14}{res}{space 2} .0020205{col 26}{space 2} .0006287{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .0007884{col 67}{space 3} .0032527
{txt}ln_functio~l {c |}{col 14}{res}{space 2}-.0235737{col 26}{space 2} .0059018{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4} -.035141{col 67}{space 3}-.0120065
{txt}t1_totalno~t {c |}{col 14}{res}{space 2} .0077345{col 26}{space 2} .0077113{col 37}{space 1}    1.00{col 46}{space 3}0.316{col 54}{space 4}-.0073794{col 67}{space 3} .0228484
{txt}t1_totalfe~t {c |}{col 14}{res}{space 2} .0267389{col 26}{space 2} .0059417{col 37}{space 1}    4.50{col 46}{space 3}0.000{col 54}{space 4} .0150934{col 67}{space 3} .0383845
{txt}t1_totalag~t {c |}{col 14}{res}{space 2} .0100197{col 26}{space 2} .0112281{col 37}{space 1}    0.89{col 46}{space 3}0.372{col 54}{space 4} -.011987{col 67}{space 3} .0320263
{txt}{space 12} {c |}
{space 5}stateid {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .0524451{col 26}{space 2} .0074355{col 37}{space 1}    7.05{col 46}{space 3}0.000{col 54}{space 4} .0378718{col 67}{space 3} .0670184
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0054002{col 26}{space 2} .0056535{col 37}{space 1}   -0.96{col 46}{space 3}0.339{col 54}{space 4}-.0164809{col 67}{space 3} .0056805
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .007515{col 26}{space 2} .0060236{col 37}{space 1}    1.25{col 46}{space 3}0.212{col 54}{space 4}-.0042909{col 67}{space 3}  .019321
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0486094{col 26}{space 2} .0146973{col 37}{space 1}   -3.31{col 46}{space 3}0.001{col 54}{space 4}-.0774155{col 67}{space 3}-.0198033
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0393005{col 26}{space 2} .0078178{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4} .0239779{col 67}{space 3} .0546232
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0182385{col 26}{space 2} .0055908{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .0072808{col 67}{space 3} .0291962
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0180798{col 26}{space 2} .0083501{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0017139{col 67}{space 3} .0344458
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0102986{col 26}{space 2} .0083732{col 37}{space 1}   -1.23{col 46}{space 3}0.219{col 54}{space 4}-.0267098{col 67}{space 3} .0061125
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0068545{col 26}{space 2} .0064021{col 37}{space 1}   -1.07{col 46}{space 3}0.284{col 54}{space 4}-.0194023{col 67}{space 3} .0056933
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0148686{col 26}{space 2}   .00649{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0021485{col 67}{space 3} .0275887
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0043219{col 26}{space 2} .0070712{col 37}{space 1}    0.61{col 46}{space 3}0.541{col 54}{space 4}-.0095374{col 67}{space 3} .0181813
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0118744{col 26}{space 2} .0090367{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4} -.029586{col 67}{space 3} .0058372
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .1512256{col 26}{space 2} .0148887{col 37}{space 1}   10.16{col 46}{space 3}0.000{col 54}{space 4} .1220444{col 67}{space 3} .1804068
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .0370124{col 26}{space 2} .0054551{col 37}{space 1}    6.78{col 46}{space 3}0.000{col 54}{space 4} .0263207{col 67}{space 3} .0477041
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .0758647{col 26}{space 2} .0107529{col 37}{space 1}    7.06{col 46}{space 3}0.000{col 54}{space 4} .0547894{col 67}{space 3}   .09694
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .0040536{col 26}{space 2} .0065659{col 37}{space 1}    0.62{col 46}{space 3}0.537{col 54}{space 4}-.0088154{col 67}{space 3} .0169226
{txt}{space 9}18  {c |}{col 14}{res}{space 2}  .094885{col 26}{space 2} .0091939{col 37}{space 1}   10.32{col 46}{space 3}0.000{col 54}{space 4} .0768653{col 67}{space 3} .1129048
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .0299287{col 26}{space 2}  .006695{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .0168068{col 67}{space 3} .0430507
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0262427{col 26}{space 2} .0051486{col 37}{space 1}   -5.10{col 46}{space 3}0.000{col 54}{space 4}-.0363338{col 67}{space 3}-.0161517
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .0197099{col 26}{space 2} .0065703{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .0068324{col 67}{space 3} .0325875
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.0411639{col 26}{space 2} .0088751{col 37}{space 1}   -4.64{col 46}{space 3}0.000{col 54}{space 4}-.0585588{col 67}{space 3}-.0237689
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.0006116{col 26}{space 2} .0068104{col 37}{space 1}   -0.09{col 46}{space 3}0.928{col 54}{space 4}-.0139597{col 67}{space 3} .0127365
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.0080532{col 26}{space 2} .0054092{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4} -.018655{col 67}{space 3} .0025487
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.0307254{col 26}{space 2}   .00465{col 37}{space 1}   -6.61{col 46}{space 3}0.000{col 54}{space 4}-.0398392{col 67}{space 3}-.0216116
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0698159{col 26}{space 2} .0078791{col 37}{space 1}    8.86{col 46}{space 3}0.000{col 54}{space 4} .0543731{col 67}{space 3} .0852587
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.0083411{col 26}{space 2}  .004318{col 37}{space 1}   -1.93{col 46}{space 3}0.053{col 54}{space 4}-.0168043{col 67}{space 3} .0001222
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .0111113{col 26}{space 2} .0076313{col 37}{space 1}    1.46{col 46}{space 3}0.145{col 54}{space 4}-.0038458{col 67}{space 3} .0260684
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.0206202{col 26}{space 2} .0079951{col 37}{space 1}   -2.58{col 46}{space 3}0.010{col 54}{space 4}-.0362902{col 67}{space 3}-.0049502
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0028953{col 26}{space 2} .0097234{col 37}{space 1}    0.30{col 46}{space 3}0.766{col 54}{space 4}-.0161623{col 67}{space 3} .0219528
{txt}{space 9}32  {c |}{col 14}{res}{space 2}-.0144059{col 26}{space 2} .0106255{col 37}{space 1}   -1.36{col 46}{space 3}0.175{col 54}{space 4}-.0352316{col 67}{space 3} .0064198
{txt}{space 9}33  {c |}{col 14}{res}{space 2} -.001036{col 26}{space 2}  .005563{col 37}{space 1}   -0.19{col 46}{space 3}0.852{col 54}{space 4}-.0119394{col 67}{space 3} .0098674
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .0412864{col 26}{space 2} .0108006{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .0201177{col 67}{space 3} .0624551
{txt}{space 9}35  {c |}{col 14}{res}{space 2}-.0069611{col 26}{space 2} .0113092{col 37}{space 1}   -0.62{col 46}{space 3}0.538{col 54}{space 4}-.0291268{col 67}{space 3} .0152045
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.0053069{col 26}{space 2} .0045045{col 37}{space 1}   -1.18{col 46}{space 3}0.239{col 54}{space 4}-.0141355{col 67}{space 3} .0035217
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .0057928{col 26}{space 2} .0059271{col 37}{space 1}    0.98{col 46}{space 3}0.328{col 54}{space 4}-.0058241{col 67}{space 3} .0174098
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .1032123{col 26}{space 2} .0160469{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4} .0717609{col 67}{space 3} .1346636
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .0283663{col 26}{space 2} .0070452{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} .0145579{col 67}{space 3} .0421747
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .0007747{col 26}{space 2} .0055391{col 37}{space 1}    0.14{col 46}{space 3}0.889{col 54}{space 4}-.0100817{col 67}{space 3} .0116311
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .0258946{col 26}{space 2} .0105243{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .0052674{col 67}{space 3} .0465218
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .0265469{col 26}{space 2} .0052258{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .0163046{col 67}{space 3} .0367893
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.0242341{col 26}{space 2} .0091257{col 37}{space 1}   -2.66{col 46}{space 3}0.008{col 54}{space 4}-.0421202{col 67}{space 3}-.0063481
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .0027669{col 26}{space 2} .0074355{col 37}{space 1}    0.37{col 46}{space 3}0.710{col 54}{space 4}-.0118065{col 67}{space 3} .0173402
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .0720565{col 26}{space 2} .0116325{col 37}{space 1}    6.19{col 46}{space 3}0.000{col 54}{space 4} .0492572{col 67}{space 3} .0948558
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .0388022{col 26}{space 2} .0063215{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} .0264123{col 67}{space 3} .0511922
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.0221478{col 26}{space 2} .0069772{col 37}{space 1}   -3.17{col 46}{space 3}0.002{col 54}{space 4}-.0358229{col 67}{space 3}-.0084728
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .0178653{col 26}{space 2} .0078176{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0025431{col 67}{space 3} .0331875
{txt}{space 9}49  {c |}{col 14}{res}{space 2} -.016679{col 26}{space 2} .0060813{col 37}{space 1}   -2.74{col 46}{space 3}0.006{col 54}{space 4}-.0285982{col 67}{space 3}-.0047598
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .0431704{col 26}{space 2} .0101687{col 37}{space 1}    4.25{col 46}{space 3}0.000{col 54}{space 4} .0232402{col 67}{space 3} .0631007
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .0837753{col 26}{space 2} .0080792{col 37}{space 1}   10.37{col 46}{space 3}0.000{col 54}{space 4} .0679404{col 67}{space 3} .0996102
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .0198637{col 26}{space 2} .0066753{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0067804{col 67}{space 3} .0329469
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2003  {c |}{col 14}{res}{space 2} .0021015{col 26}{space 2}  .003186{col 37}{space 1}    0.66{col 46}{space 3}0.510{col 54}{space 4} -.004143{col 67}{space 3} .0083459
{txt}{space 7}2004  {c |}{col 14}{res}{space 2}-.0015453{col 26}{space 2} .0032033{col 37}{space 1}   -0.48{col 46}{space 3}0.630{col 54}{space 4}-.0078236{col 67}{space 3}  .004733
{txt}{space 7}2005  {c |}{col 14}{res}{space 2} .0000522{col 26}{space 2} .0036009{col 37}{space 1}    0.01{col 46}{space 3}0.988{col 54}{space 4}-.0070054{col 67}{space 3} .0071099
{txt}{space 7}2006  {c |}{col 14}{res}{space 2}-.0002675{col 26}{space 2} .0037881{col 37}{space 1}   -0.07{col 46}{space 3}0.944{col 54}{space 4}-.0076921{col 67}{space 3} .0071571
{txt}{space 7}2007  {c |}{col 14}{res}{space 2}-.0085457{col 26}{space 2} .0031311{col 37}{space 1}   -2.73{col 46}{space 3}0.006{col 54}{space 4}-.0146825{col 67}{space 3} -.002409
{txt}{space 7}2008  {c |}{col 14}{res}{space 2}-.0088991{col 26}{space 2} .0034315{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-.0156248{col 67}{space 3}-.0021734
{txt}{space 7}2009  {c |}{col 14}{res}{space 2} -.008576{col 26}{space 2} .0042026{col 37}{space 1}   -2.04{col 46}{space 3}0.041{col 54}{space 4} -.016813{col 67}{space 3}-.0003389
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} .0011239{col 26}{space 2} .0044783{col 37}{space 1}    0.25{col 46}{space 3}0.802{col 54}{space 4}-.0076534{col 67}{space 3} .0099011
{txt}{space 7}2011  {c |}{col 14}{res}{space 2}-.0098193{col 26}{space 2}  .004041{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-.0177395{col 67}{space 3}-.0018991
{txt}{space 7}2012  {c |}{col 14}{res}{space 2}-.0120909{col 26}{space 2} .0040568{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-.0200422{col 67}{space 3}-.0041396
{txt}{space 7}2013  {c |}{col 14}{res}{space 2}-.0173894{col 26}{space 2} .0039514{col 37}{space 1}   -4.40{col 46}{space 3}0.000{col 54}{space 4} -.025134{col 67}{space 3}-.0096447
{txt}{space 7}2014  {c |}{col 14}{res}{space 2}-.0108199{col 26}{space 2} .0042997{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.0192472{col 67}{space 3}-.0023927
{txt}{space 7}2015  {c |}{col 14}{res}{space 2}-.0080526{col 26}{space 2} .0045838{col 37}{space 1}   -1.76{col 46}{space 3}0.079{col 54}{space 4}-.0170367{col 67}{space 3} .0009315
{txt}{space 7}2016  {c |}{col 14}{res}{space 2}-.0073068{col 26}{space 2} .0047205{col 37}{space 1}   -1.55{col 46}{space 3}0.122{col 54}{space 4}-.0165588{col 67}{space 3} .0019452
{txt}{space 7}2017  {c |}{col 14}{res}{space 2}-.0024523{col 26}{space 2} .0046644{col 37}{space 1}   -0.53{col 46}{space 3}0.599{col 54}{space 4}-.0115943{col 67}{space 3} .0066898
{txt}{space 7}2018  {c |}{col 14}{res}{space 2}-.0051118{col 26}{space 2} .0052406{col 37}{space 1}   -0.98{col 46}{space 3}0.329{col 54}{space 4}-.0153832{col 67}{space 3} .0051597
{txt}{space 7}2019  {c |}{col 14}{res}{space 2}-.0161491{col 26}{space 2} .0046867{col 37}{space 1}   -3.45{col 46}{space 3}0.001{col 54}{space 4}-.0253349{col 67}{space 3}-.0069633
{txt}{space 7}2020  {c |}{col 14}{res}{space 2}  .014654{col 26}{space 2}  .005697{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4}  .003488{col 67}{space 3} .0258199
{txt}{space 7}2021  {c |}{col 14}{res}{space 2} .0608665{col 26}{space 2}  .006526{col 37}{space 1}    9.33{col 46}{space 3}0.000{col 54}{space 4} .0480758{col 67}{space 3} .0736572
{txt}{space 7}2022  {c |}{col 14}{res}{space 2} .0278812{col 26}{space 2} .0070057{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0141503{col 67}{space 3} .0416122
{txt}{space 12} {c |}
ad~2_itadopt {c |}{col 14}{res}{space 2} .0525378{col 26}{space 2} .0094796{col 37}{space 1}    5.54{col 46}{space 3}0.000{col 54}{space 4}  .033958{col 67}{space 3} .0711175
{txt}a~04_itadopt {c |}{col 14}{res}{space 2} .0294467{col 26}{space 2} .0098863{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0100699{col 67}{space 3} .0488236
{txt}a~06_itadopt {c |}{col 14}{res}{space 2} .0039115{col 26}{space 2} .0069927{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-.0097939{col 67}{space 3} .0176169
{txt}a~07_itadopt {c |}{col 14}{res}{space 2} .0196476{col 26}{space 2} .0068411{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0062393{col 67}{space 3} .0330558
{txt}ad~9_itadopt {c |}{col 14}{res}{space 2}  .004954{col 26}{space 2} .0051461{col 37}{space 1}    0.96{col 46}{space 3}0.336{col 54}{space 4}-.0051322{col 67}{space 3} .0150401
{txt}a~10_itadopt {c |}{col 14}{res}{space 2} .0039988{col 26}{space 2} .0069096{col 37}{space 1}    0.58{col 46}{space 3}0.563{col 54}{space 4}-.0095438{col 67}{space 3} .0175414
{txt}ad~3_itadopt {c |}{col 14}{res}{space 2} .0245422{col 26}{space 2} .0052585{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .0142357{col 67}{space 3} .0348486
{txt}a~14_itadopt {c |}{col 14}{res}{space 2}-.0746873{col 26}{space 2} .0182441{col 37}{space 1}   -4.09{col 46}{space 3}0.000{col 54}{space 4}-.1104451{col 67}{space 3}-.0389294
{txt}ad~5_itadopt {c |}{col 14}{res}{space 2}-.0190777{col 26}{space 2}  .007517{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.0338108{col 67}{space 3}-.0043447
{txt}a~16_itadopt {c |}{col 14}{res}{space 2}-.0032107{col 26}{space 2} .0064995{col 37}{space 1}   -0.49{col 46}{space 3}0.621{col 54}{space 4}-.0159494{col 67}{space 3}  .009528
{txt}a~17_itadopt {c |}{col 14}{res}{space 2}-.0177476{col 26}{space 2} .0052137{col 37}{space 1}   -3.40{col 46}{space 3}0.001{col 54}{space 4}-.0279663{col 67}{space 3}-.0075289
{txt}ad~8_itadopt {c |}{col 14}{res}{space 2}-.0346753{col 26}{space 2}  .006911{col 37}{space 1}   -5.02{col 46}{space 3}0.000{col 54}{space 4}-.0482207{col 67}{space 3}-.0211299
{txt}a~20_itadopt {c |}{col 14}{res}{space 2}  .083107{col 26}{space 2} .0173899{col 37}{space 1}    4.78{col 46}{space 3}0.000{col 54}{space 4} .0490235{col 67}{space 3} .1171905
{txt}ad~1_itadopt {c |}{col 14}{res}{space 2}-.0503196{col 26}{space 2} .0261904{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-.1016517{col 67}{space 3} .0010126
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m2c if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m2c if e(sample), residuals
{res}{txt}(196 missing values generated)

{com}. 
. gen sse_m2c = predsy_m2c * predsy_m2c if e(sample)
{txt}(196 missing values generated)

{com}. gen ssr_m2c = residsy_m2c * residsy_m2c if e(sample)
{txt}(196 missing values generated)

{com}. 
. egen sum_sse_m2c = total(sse_m2c) if e(sample)
{txt}(196 missing values generated)

{com}. egen sum_ssr_m2c = total(ssr_m2c) if e(sample)
{txt}(196 missing values generated)

{com}. 
. gen r2_m2c = sum_ssr_m2c/(sum_sse_m2c + sum_ssr_m2c)
{txt}(196 missing values generated)

{com}. 
. sum r2_m2c

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m2c {c |}{res}     12,355    .4919091           0   .4919091   .4919091
{txt}
{com}. 
. 
. * [MODEL C2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE C2A: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:12,355}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0542955{col 26}{space 2} .0010966{col 37}{space 1}   49.51{col 46}{space 3}0.000{col 54}{space 4} .0521463{col 67}{space 3} .0564448
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0537547{col 26}{space 2} .0009779{col 37}{space 1}   54.97{col 46}{space 3}0.000{col 54}{space 4} .0518381{col 67}{space 3} .0556714
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0512829{col 26}{space 2} .0007363{col 37}{space 1}   69.65{col 46}{space 3}0.000{col 54}{space 4} .0498398{col 67}{space 3} .0527261
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0488017{col 26}{space 2} .0011867{col 37}{space 1}   41.12{col 46}{space 3}0.000{col 54}{space 4} .0464759{col 67}{space 3} .0511276
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0468094{col 26}{space 2} .0017893{col 37}{space 1}   26.16{col 46}{space 3}0.000{col 54}{space 4} .0433024{col 67}{space 3} .0503164
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0452634{col 26}{space 2} .0023379{col 37}{space 1}   19.36{col 46}{space 3}0.000{col 54}{space 4} .0406812{col 67}{space 3} .0498457
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0441213{col 26}{space 2} .0028045{col 37}{space 1}   15.73{col 46}{space 3}0.000{col 54}{space 4} .0386246{col 67}{space 3}  .049618
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0433404{col 26}{space 2} .0031885{col 37}{space 1}   13.59{col 46}{space 3}0.000{col 54}{space 4} .0370911{col 67}{space 3} .0495898
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0428783{col 26}{space 2} .0034959{col 37}{space 1}   12.27{col 46}{space 3}0.000{col 54}{space 4} .0360266{col 67}{space 3} .0497301
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0426925{col 26}{space 2} .0037347{col 37}{space 1}   11.43{col 46}{space 3}0.000{col 54}{space 4} .0353726{col 67}{space 3} .0500124
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0427404{col 26}{space 2} .0039142{col 37}{space 1}   10.92{col 46}{space 3}0.000{col 54}{space 4} .0350687{col 67}{space 3}  .050412
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0429794{col 26}{space 2} .0040437{col 37}{space 1}   10.63{col 46}{space 3}0.000{col 54}{space 4} .0350539{col 67}{space 3} .0509049
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE C2A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Absolute Type I Program Error Rate [MODEL C2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2A.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2A.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2A.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. * [MODEL C2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE C2B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_catC==2) & LOW COMPLEXITY (t1_interstate_catC==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_interstate_catC if t1_interstate_catC==0|t1_interstate_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,299}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 23}{c TT}{hline 11}{hline 12}{hline 11}
{col 24}{text}{c |}         df{col 36}        chi2{col 48}     P>chi2
{res}{col 1}{text}{hline 23}{c +}{hline 11}{hline 12}{hline 11}
t1_interstate_catC@_at {c |}
{space 10}(2 vs 0)  1  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.83{col 48}{space 2}   0.1756
{txt}{space 10}(2 vs 0)  2  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     2.02{col 48}{space 2}   0.1554
{txt}{space 10}(2 vs 0)  3  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     2.16{col 48}{space 2}   0.1412
{txt}{space 10}(2 vs 0)  4  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.28{col 48}{space 2}   0.2577
{txt}{space 10}(2 vs 0)  5  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.57{col 48}{space 2}   0.4507
{txt}{space 10}(2 vs 0)  6  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.18{col 48}{space 2}   0.6741
{txt}{space 10}(2 vs 0)  7  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.01{col 48}{space 2}   0.9106
{txt}{space 10}(2 vs 0)  8  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.04{col 48}{space 2}   0.8486
{txt}{space 10}(2 vs 0)  9  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.25{col 48}{space 2}   0.6158
{txt}{space 10}(2 vs 0) 10  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.68{col 48}{space 2}   0.4080
{txt}{space 10}(2 vs 0) 11  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.37{col 48}{space 2}   0.2424
{txt}{space 10}(2 vs 0) 12  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     2.32{col 48}{space 2}   0.1279
{col 1}{text}                Joint {col 24}{c |}{result}  (not testable)
{col 1}{text}{hline 23}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}   Contrast{col 36}   std. err.{col 48}     [95% con{col 61}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_interstate_catC@_at {c |}
{space 10}(2 vs 0)  1  {c |}{col 24}{res}{space 2} .0031948{col 36}{space 2} .0023586{col 47}{space 5} -.001428{col 61}{space 3} .0078176
{txt}{space 10}(2 vs 0)  2  {c |}{col 24}{res}{space 2} .0032488{col 36}{space 2} .0022866{col 47}{space 5} -.001233{col 61}{space 3} .0077305
{txt}{space 10}(2 vs 0)  3  {c |}{col 24}{res}{space 2}  .003348{col 36}{space 2} .0022756{col 47}{space 5}-.0011121{col 61}{space 3} .0078081
{txt}{space 10}(2 vs 0)  4  {c |}{col 24}{res}{space 2} .0031154{col 36}{space 2} .0027527{col 47}{space 5}-.0022796{col 61}{space 3} .0085105
{txt}{space 10}(2 vs 0)  5  {c |}{col 24}{res}{space 2} .0025359{col 36}{space 2} .0033623{col 47}{space 5}-.0040541{col 61}{space 3} .0091258
{txt}{space 10}(2 vs 0)  6  {c |}{col 24}{res}{space 2}  .001648{col 36}{space 2} .0039182{col 47}{space 5}-.0060315{col 61}{space 3} .0093274
{txt}{space 10}(2 vs 0)  7  {c |}{col 24}{res}{space 2} .0004905{col 36}{space 2} .0043676{col 47}{space 5}-.0080698{col 61}{space 3} .0090508
{txt}{space 10}(2 vs 0)  8  {c |}{col 24}{res}{space 2}-.0008979{col 36}{space 2}  .004704{col 47}{space 5}-.0101176{col 61}{space 3} .0083218
{txt}{space 10}(2 vs 0)  9  {c |}{col 24}{res}{space 2}-.0024783{col 36}{space 2} .0049387{col 47}{space 5}-.0121579{col 61}{space 3} .0072012
{txt}{space 10}(2 vs 0) 10  {c |}{col 24}{res}{space 2}-.0042122{col 36}{space 2} .0050908{col 47}{space 5}-.0141899{col 61}{space 3} .0057655
{txt}{space 10}(2 vs 0) 11  {c |}{col 24}{res}{space 2}-.0060608{col 36}{space 2} .0051842{col 47}{space 5}-.0162216{col 61}{space 3} .0041001
{txt}{space 10}(2 vs 0) 12  {c |}{col 24}{res}{space 2}-.0079852{col 36}{space 2} .0052452{col 47}{space 5}-.0182657{col 61}{space 3} .0022952
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C2B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL C2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2B.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2B.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2B.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. * [MODEL C2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE C2C: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_catC==2) & LOW COMPLEXITY (t1_diffoccupseek_catC==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_diffoccupseek_catC if t1_diffoccupseek_catC==0|t1_diffoccupseek_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,245}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
t1_diffoccupseek_catC@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    18.72{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    18.99{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    14.55{col 51}{space 2}   0.0001
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     7.31{col 51}{space 2}   0.0068
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.74{col 51}{space 2}   0.0530
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.12{col 51}{space 2}   0.1455
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.30{col 51}{space 2}   0.2534
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.85{col 51}{space 2}   0.3559
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.58{col 51}{space 2}   0.4461
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.41{col 51}{space 2}   0.5241
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.29{col 51}{space 2}   0.5918
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.20{col 51}{space 2}   0.6513
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_diffoccupseek_catC@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2} .0093806{col 39}{space 2}  .002168{col 50}{space 5} .0051314{col 64}{space 3} .0136297
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2} .0092031{col 39}{space 2} .0021119{col 50}{space 5} .0050638{col 64}{space 3} .0133424
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2} .0083508{col 39}{space 2} .0021891{col 50}{space 5} .0040603{col 64}{space 3} .0126414
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2} .0074033{col 39}{space 2} .0027375{col 50}{space 5} .0020378{col 64}{space 3} .0127687
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2} .0065343{col 39}{space 2} .0033771{col 50}{space 5}-.0000846{col 64}{space 3} .0131532
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2} .0057405{col 39}{space 2} .0039433{col 50}{space 5}-.0019882{col 64}{space 3} .0134693
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2} .0050184{col 39}{space 2} .0043943{col 50}{space 5}-.0035941{col 64}{space 3}  .013631
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2} .0043645{col 39}{space 2} .0047274{col 50}{space 5} -.004901{col 64}{space 3} .0136301
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2} .0037753{col 39}{space 2} .0049553{col 50}{space 5} -.005937{col 64}{space 3} .0134876
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2} .0032473{col 39}{space 2} .0050977{col 50}{space 5}-.0067439{col 64}{space 3} .0132385
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2}  .002777{col 39}{space 2} .0051782{col 50}{space 5} -.007372{col 64}{space 3}  .012926
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2}  .002361{col 39}{space 2} .0052232{col 50}{space 5}-.0078763{col 64}{space 3} .0125982
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C2C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL C2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2C.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2C.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C2.FIGURE C2C.12-07-2024.gph} saved

{com}. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** TESTING H2 & H4: RELATIVE TYPE I ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION ***
. 
. 
. 
. 
. *** ESTIMATE MODEL C3: RELATIVE TYPE I ERROR RATE [MODEL 3 with ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT UNIT EFFECTS] ***  (FIGURES C2D-C2F) 
. 
. 
. npregress series relt1error_rat  itmod_monthcount  i.relt1_interstate_catC i.relt1_diffoccupseek_catC, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}510{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}520{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}530{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}540{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}550{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}560{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}570{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}580{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}590{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}600{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}610{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}620{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}630{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}640{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}650{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}660{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}670{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}680{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}690{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}700{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}710{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}720{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}730{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}740{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}750{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}760{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}770{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}780{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}790{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}800{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}810{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}820{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}830{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}840{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}850{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}860{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}870{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}880{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}890{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}900{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}910{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}920{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}930{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}940{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}950{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}960{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}970{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}980{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}990{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}1,000{text} done
{res}
{txt}Cubic B-spline estimation {col 44}Number of obs      =  {res}       11,590
{txt}Criterion: {res:cross validation}{col 44}Number of knots    =  {res}            1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}relt1error~t{col 14}{c |}     effect{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
itmod_mon~nt {c |}{col 14}{res}{space 2}-.0010564{col 26}{space 2} .0006048{col 37}{space 1}   -1.75{col 46}{space 3}0.081{col 54}{space 4}-.0022417{col 67}{space 3} .0001289
{txt}{space 12} {c |}
relt1_inte~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.014344{col 26}{space 2} .0057055{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4}-.0255267{col 67}{space 3}-.0031614
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  -.02474{col 26}{space 2} .0081615{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.0407363{col 67}{space 3}-.0087438
{txt}{space 12} {c |}
relt1_diff~C {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0158486{col 26}{space 2} .0061232{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-.0278498{col 67}{space 3}-.0038474
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0015806{col 26}{space 2} .0076175{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}-.0165107{col 67}{space 3} .0133495
{txt}{space 12} {c |}
{space 1}demgovparty {c |}{col 14}{res}{space 2}-.0366858{col 26}{space 2} .0268025{col 37}{space 1}   -1.37{col 46}{space 3}0.171{col 54}{space 4}-.0892178{col 67}{space 3} .0158461
{txt}{space 1}repgovparty {c |}{col 14}{res}{space 2}-.0466647{col 26}{space 2}  .026707{col 37}{space 1}   -1.75{col 46}{space 3}0.081{col 54}{space 4}-.0990093{col 67}{space 3}   .00568
{txt}{space 1}ln_workload {c |}{col 14}{res}{space 2}-.0158039{col 26}{space 2} .0063682{col 37}{space 1}   -2.48{col 46}{space 3}0.013{col 54}{space 4}-.0282854{col 67}{space 3}-.0033224
{txt}automation~e {c |}{col 14}{res}{space 2} .0243143{col 26}{space 2} .0140494{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4} -.003222{col 67}{space 3} .0518507
{txt}ln_uiadmin~l {c |}{col 14}{res}{space 2}-.0134855{col 26}{space 2} .0178739{col 37}{space 1}   -0.75{col 46}{space 3}0.451{col 54}{space 4}-.0485176{col 67}{space 3} .0215466
{txt}benefitgen~2 {c |}{col 14}{res}{space 2} .0630443{col 26}{space 2} .0310052{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4} .0022752{col 67}{space 3} .1238134
{txt}{space 2}unemp_rate {c |}{col 14}{res}{space 2} .0008639{col 26}{space 2} .0026633{col 37}{space 1}    0.32{col 46}{space 3}0.746{col 54}{space 4} -.004356{col 67}{space 3} .0060839
{txt}ln_functio~l {c |}{col 14}{res}{space 2}-.0085984{col 26}{space 2} .0190441{col 37}{space 1}   -0.45{col 46}{space 3}0.652{col 54}{space 4}-.0459241{col 67}{space 3} .0287272
{txt}tot_totaln~t {c |}{col 14}{res}{space 2}-.0003304{col 26}{space 2} .0142834{col 37}{space 1}   -0.02{col 46}{space 3}0.982{col 54}{space 4}-.0283253{col 67}{space 3} .0276646
{txt}tot_totalf~t {c |}{col 14}{res}{space 2} .0601048{col 26}{space 2} .0168733{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .0270337{col 67}{space 3} .0931759
{txt}tot_totala~t {c |}{col 14}{res}{space 2} .0043094{col 26}{space 2} .0249993{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4}-.0446884{col 67}{space 3} .0533072
{txt}{space 12} {c |}
{space 5}stateid {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.0689684{col 26}{space 2} .0317549{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.1312069{col 67}{space 3}  -.00673
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0985488{col 26}{space 2} .0316325{col 37}{space 1}   -3.12{col 46}{space 3}0.002{col 54}{space 4}-.1605473{col 67}{space 3}-.0365503
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1986888{col 26}{space 2} .0292257{col 37}{space 1}   -6.80{col 46}{space 3}0.000{col 54}{space 4}-.2559702{col 67}{space 3}-.1414074
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1275367{col 26}{space 2} .0580683{col 37}{space 1}   -2.20{col 46}{space 3}0.028{col 54}{space 4}-.2413485{col 67}{space 3}-.0137248
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0376648{col 26}{space 2} .0322358{col 37}{space 1}   -1.17{col 46}{space 3}0.243{col 54}{space 4}-.1008458{col 67}{space 3} .0255162
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.1052342{col 26}{space 2} .0317408{col 37}{space 1}   -3.32{col 46}{space 3}0.001{col 54}{space 4}-.1674451{col 67}{space 3}-.0430234
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.2125682{col 26}{space 2} .0361302{col 37}{space 1}   -5.88{col 46}{space 3}0.000{col 54}{space 4} -.283382{col 67}{space 3}-.1417544
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0710006{col 26}{space 2} .0374972{col 37}{space 1}   -1.89{col 46}{space 3}0.058{col 54}{space 4}-.1444937{col 67}{space 3} .0024926
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.1477015{col 26}{space 2} .0317979{col 37}{space 1}   -4.65{col 46}{space 3}0.000{col 54}{space 4}-.2100243{col 67}{space 3}-.0853787
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.2539592{col 26}{space 2} .0297531{col 37}{space 1}   -8.54{col 46}{space 3}0.000{col 54}{space 4}-.3122743{col 67}{space 3}-.1956442
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.1502178{col 26}{space 2} .0350369{col 37}{space 1}   -4.29{col 46}{space 3}0.000{col 54}{space 4}-.2188888{col 67}{space 3}-.0815468
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.1299068{col 26}{space 2} .0403466{col 37}{space 1}   -3.22{col 46}{space 3}0.001{col 54}{space 4}-.2089847{col 67}{space 3}-.0508289
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .1376353{col 26}{space 2} .0335824{col 37}{space 1}    4.10{col 46}{space 3}0.000{col 54}{space 4} .0718151{col 67}{space 3} .2034555
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.1161954{col 26}{space 2} .0280526{col 37}{space 1}   -4.14{col 46}{space 3}0.000{col 54}{space 4}-.1711774{col 67}{space 3}-.0612133
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .0414899{col 26}{space 2} .0366576{col 37}{space 1}    1.13{col 46}{space 3}0.258{col 54}{space 4}-.0303576{col 67}{space 3} .1133374
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.1033633{col 26}{space 2} .0339134{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.1698324{col 67}{space 3}-.0368943
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.0395583{col 26}{space 2} .0290448{col 37}{space 1}   -1.36{col 46}{space 3}0.173{col 54}{space 4} -.096485{col 67}{space 3} .0173683
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.2645835{col 26}{space 2} .0310968{col 37}{space 1}   -8.51{col 46}{space 3}0.000{col 54}{space 4}-.3255321{col 67}{space 3}-.2036348
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.2104126{col 26}{space 2} .0284611{col 37}{space 1}   -7.39{col 46}{space 3}0.000{col 54}{space 4}-.2661954{col 67}{space 3}-.1546299
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1822643{col 26}{space 2} .0316266{col 37}{space 1}   -5.76{col 46}{space 3}0.000{col 54}{space 4}-.2442513{col 67}{space 3}-.1202773
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.1154024{col 26}{space 2} .0397296{col 37}{space 1}   -2.90{col 46}{space 3}0.004{col 54}{space 4}-.1932709{col 67}{space 3}-.0375339
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.1695532{col 26}{space 2} .0362236{col 37}{space 1}   -4.68{col 46}{space 3}0.000{col 54}{space 4}  -.24055{col 67}{space 3}-.0985563
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .0643311{col 26}{space 2} .0442617{col 37}{space 1}    1.45{col 46}{space 3}0.146{col 54}{space 4}-.0224201{col 67}{space 3} .1510824
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.2668763{col 26}{space 2} .0300507{col 37}{space 1}   -8.88{col 46}{space 3}0.000{col 54}{space 4}-.3257746{col 67}{space 3}-.2079781
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.0238732{col 26}{space 2} .0356442{col 37}{space 1}   -0.67{col 46}{space 3}0.503{col 54}{space 4}-.0937345{col 67}{space 3}  .045988
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.2055686{col 26}{space 2} .0282502{col 37}{space 1}   -7.28{col 46}{space 3}0.000{col 54}{space 4}-.2609381{col 67}{space 3}-.1501992
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.2476538{col 26}{space 2} .0393433{col 37}{space 1}   -6.29{col 46}{space 3}0.000{col 54}{space 4}-.3247652{col 67}{space 3}-.1705424
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.1559712{col 26}{space 2} .0375339{col 37}{space 1}   -4.16{col 46}{space 3}0.000{col 54}{space 4}-.2295364{col 67}{space 3} -.082406
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.2355718{col 26}{space 2} .1039465{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.4393032{col 67}{space 3}-.0318405
{txt}{space 9}32  {c |}{col 14}{res}{space 2}-.0847576{col 26}{space 2} .0452514{col 37}{space 1}   -1.87{col 46}{space 3}0.061{col 54}{space 4}-.1734487{col 67}{space 3} .0039336
{txt}{space 9}33  {c |}{col 14}{res}{space 2} -.068803{col 26}{space 2} .0330269{col 37}{space 1}   -2.08{col 46}{space 3}0.037{col 54}{space 4}-.1335345{col 67}{space 3}-.0040714
{txt}{space 9}34  {c |}{col 14}{res}{space 2}-.1174059{col 26}{space 2} .0439936{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.2036318{col 67}{space 3}-.0311801
{txt}{space 9}35  {c |}{col 14}{res}{space 2}-.1802728{col 26}{space 2} .0492504{col 37}{space 1}   -3.66{col 46}{space 3}0.000{col 54}{space 4}-.2768019{col 67}{space 3}-.0837437
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.1696092{col 26}{space 2} .0313287{col 37}{space 1}   -5.41{col 46}{space 3}0.000{col 54}{space 4}-.2310124{col 67}{space 3}-.1082061
{txt}{space 9}37  {c |}{col 14}{res}{space 2}-.0989885{col 26}{space 2} .0331333{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.1639286{col 67}{space 3}-.0340485
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .0151776{col 26}{space 2} .0453428{col 37}{space 1}    0.33{col 46}{space 3}0.738{col 54}{space 4}-.0736927{col 67}{space 3} .1040479
{txt}{space 9}39  {c |}{col 14}{res}{space 2}-.1506758{col 26}{space 2} .0328692{col 37}{space 1}   -4.58{col 46}{space 3}0.000{col 54}{space 4}-.2150982{col 67}{space 3}-.0862534
{txt}{space 9}40  {c |}{col 14}{res}{space 2}-.0996526{col 26}{space 2} .0297472{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4} -.157956{col 67}{space 3}-.0413493
{txt}{space 9}41  {c |}{col 14}{res}{space 2}-.2440987{col 26}{space 2} .0421618{col 37}{space 1}   -5.79{col 46}{space 3}0.000{col 54}{space 4}-.3267344{col 67}{space 3} -.161463
{txt}{space 9}42  {c |}{col 14}{res}{space 2}-.1664619{col 26}{space 2} .0285792{col 37}{space 1}   -5.82{col 46}{space 3}0.000{col 54}{space 4}-.2224761{col 67}{space 3}-.1104477
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.0447775{col 26}{space 2} .0396684{col 37}{space 1}   -1.13{col 46}{space 3}0.259{col 54}{space 4}-.1225261{col 67}{space 3} .0329711
{txt}{space 9}44  {c |}{col 14}{res}{space 2}-.2318023{col 26}{space 2} .0450164{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-.3200329{col 67}{space 3}-.1435717
{txt}{space 9}45  {c |}{col 14}{res}{space 2}-.1223305{col 26}{space 2} .0430642{col 37}{space 1}   -2.84{col 46}{space 3}0.005{col 54}{space 4}-.2067348{col 67}{space 3}-.0379261
{txt}{space 9}46  {c |}{col 14}{res}{space 2}-.0072219{col 26}{space 2} .0304436{col 37}{space 1}   -0.24{col 46}{space 3}0.812{col 54}{space 4}-.0668902{col 67}{space 3} .0524465
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.2133993{col 26}{space 2} .0350222{col 37}{space 1}   -6.09{col 46}{space 3}0.000{col 54}{space 4}-.2820415{col 67}{space 3}-.1447571
{txt}{space 9}48  {c |}{col 14}{res}{space 2}-.2567337{col 26}{space 2} .0351907{col 37}{space 1}   -7.30{col 46}{space 3}0.000{col 54}{space 4}-.3257062{col 67}{space 3}-.1877611
{txt}{space 9}49  {c |}{col 14}{res}{space 2} -.169383{col 26}{space 2} .0320351{col 37}{space 1}   -5.29{col 46}{space 3}0.000{col 54}{space 4}-.2321705{col 67}{space 3}-.1065954
{txt}{space 9}50  {c |}{col 14}{res}{space 2}-.0710934{col 26}{space 2} .0431495{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.1556648{col 67}{space 3} .0134781
{txt}{space 9}51  {c |}{col 14}{res}{space 2}-.0039885{col 26}{space 2} .0342274{col 37}{space 1}   -0.12{col 46}{space 3}0.907{col 54}{space 4} -.071073{col 67}{space 3} .0630959
{txt}{space 9}52  {c |}{col 14}{res}{space 2} -.140596{col 26}{space 2} .0313133{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.2019689{col 67}{space 3}-.0792231
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2003  {c |}{col 14}{res}{space 2} .0006096{col 26}{space 2}  .015696{col 37}{space 1}    0.04{col 46}{space 3}0.969{col 54}{space 4}-.0301539{col 67}{space 3} .0313731
{txt}{space 7}2004  {c |}{col 14}{res}{space 2}-.0352393{col 26}{space 2} .0158611{col 37}{space 1}   -2.22{col 46}{space 3}0.026{col 54}{space 4}-.0663265{col 67}{space 3} -.004152
{txt}{space 7}2005  {c |}{col 14}{res}{space 2}-.0342671{col 26}{space 2} .0165046{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.0666156{col 67}{space 3}-.0019185
{txt}{space 7}2006  {c |}{col 14}{res}{space 2}-.0531054{col 26}{space 2} .0158701{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.0842102{col 67}{space 3}-.0220006
{txt}{space 7}2007  {c |}{col 14}{res}{space 2}-.0693003{col 26}{space 2} .0163489{col 37}{space 1}   -4.24{col 46}{space 3}0.000{col 54}{space 4}-.1013436{col 67}{space 3} -.037257
{txt}{space 7}2008  {c |}{col 14}{res}{space 2}-.0695302{col 26}{space 2}  .016677{col 37}{space 1}   -4.17{col 46}{space 3}0.000{col 54}{space 4}-.1022166{col 67}{space 3}-.0368437
{txt}{space 7}2009  {c |}{col 14}{res}{space 2}-.0457232{col 26}{space 2} .0184942{col 37}{space 1}   -2.47{col 46}{space 3}0.013{col 54}{space 4}-.0819712{col 67}{space 3}-.0094752
{txt}{space 7}2010  {c |}{col 14}{res}{space 2}-.0344609{col 26}{space 2} .0186739{col 37}{space 1}   -1.85{col 46}{space 3}0.065{col 54}{space 4}-.0710611{col 67}{space 3} .0021393
{txt}{space 7}2011  {c |}{col 14}{res}{space 2}-.0409107{col 26}{space 2} .0183177{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.0768128{col 67}{space 3}-.0050087
{txt}{space 7}2012  {c |}{col 14}{res}{space 2}-.0551158{col 26}{space 2} .0181663{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.0907211{col 67}{space 3}-.0195105
{txt}{space 7}2013  {c |}{col 14}{res}{space 2}-.0719701{col 26}{space 2} .0188432{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.1089021{col 67}{space 3}-.0350382
{txt}{space 7}2014  {c |}{col 14}{res}{space 2}-.0653833{col 26}{space 2} .0180811{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4}-.1008215{col 67}{space 3} -.029945
{txt}{space 7}2015  {c |}{col 14}{res}{space 2}-.0497014{col 26}{space 2} .0188729{col 37}{space 1}   -2.63{col 46}{space 3}0.008{col 54}{space 4}-.0866915{col 67}{space 3}-.0127113
{txt}{space 7}2016  {c |}{col 14}{res}{space 2}-.0610154{col 26}{space 2} .0188487{col 37}{space 1}   -3.24{col 46}{space 3}0.001{col 54}{space 4}-.0979581{col 67}{space 3}-.0240727
{txt}{space 7}2017  {c |}{col 14}{res}{space 2}-.0525234{col 26}{space 2}  .020245{col 37}{space 1}   -2.59{col 46}{space 3}0.009{col 54}{space 4}-.0922029{col 67}{space 3}-.0128438
{txt}{space 7}2018  {c |}{col 14}{res}{space 2}-.0667209{col 26}{space 2} .0212699{col 37}{space 1}   -3.14{col 46}{space 3}0.002{col 54}{space 4}-.1084092{col 67}{space 3}-.0250327
{txt}{space 7}2019  {c |}{col 14}{res}{space 2}-.0990363{col 26}{space 2} .0210892{col 37}{space 1}   -4.70{col 46}{space 3}0.000{col 54}{space 4}-.1403704{col 67}{space 3}-.0577023
{txt}{space 7}2020  {c |}{col 14}{res}{space 2}-.0175135{col 26}{space 2}  .024964{col 37}{space 1}   -0.70{col 46}{space 3}0.483{col 54}{space 4}-.0664421{col 67}{space 3} .0314151
{txt}{space 7}2021  {c |}{col 14}{res}{space 2} .1356924{col 26}{space 2} .0237097{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .0892222{col 67}{space 3} .1821627
{txt}{space 7}2022  {c |}{col 14}{res}{space 2} .0242967{col 26}{space 2} .0251516{col 37}{space 1}    0.97{col 46}{space 3}0.334{col 54}{space 4}-.0249996{col 67}{space 3}  .073593
{txt}{space 12} {c |}
ad~2_itadopt {c |}{col 14}{res}{space 2} .1725303{col 26}{space 2} .1032784{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0298915{col 67}{space 3} .3749522
{txt}a~04_itadopt {c |}{col 14}{res}{space 2} .1865491{col 26}{space 2}  .043414{col 37}{space 1}    4.30{col 46}{space 3}0.000{col 54}{space 4} .1014592{col 67}{space 3}  .271639
{txt}a~06_itadopt {c |}{col 14}{res}{space 2} .2041199{col 26}{space 2} .0497391{col 37}{space 1}    4.10{col 46}{space 3}0.000{col 54}{space 4}  .106633{col 67}{space 3} .3016067
{txt}a~07_itadopt {c |}{col 14}{res}{space 2} .0229605{col 26}{space 2} .0285228{col 37}{space 1}    0.80{col 46}{space 3}0.421{col 54}{space 4}-.0329432{col 67}{space 3} .0788643
{txt}ad~9_itadopt {c |}{col 14}{res}{space 2}-.0755688{col 26}{space 2} .0329991{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4}-.1402458{col 67}{space 3}-.0108918
{txt}a~10_itadopt {c |}{col 14}{res}{space 2}-.0630251{col 26}{space 2} .0270687{col 37}{space 1}   -2.33{col 46}{space 3}0.020{col 54}{space 4}-.1160788{col 67}{space 3}-.0099714
{txt}ad~3_itadopt {c |}{col 14}{res}{space 2} .0269466{col 26}{space 2}  .019636{col 37}{space 1}    1.37{col 46}{space 3}0.170{col 54}{space 4}-.0115392{col 67}{space 3} .0654323
{txt}a~14_itadopt {c |}{col 14}{res}{space 2} .0300604{col 26}{space 2} .0347237{col 37}{space 1}    0.87{col 46}{space 3}0.387{col 54}{space 4}-.0379969{col 67}{space 3} .0981177
{txt}ad~5_itadopt {c |}{col 14}{res}{space 2}-.0609527{col 26}{space 2} .0255015{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.1109347{col 67}{space 3}-.0109706
{txt}a~16_itadopt {c |}{col 14}{res}{space 2} .0741929{col 26}{space 2}  .030113{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .0151725{col 67}{space 3} .1332134
{txt}a~17_itadopt {c |}{col 14}{res}{space 2} .0218659{col 26}{space 2} .0304153{col 37}{space 1}    0.72{col 46}{space 3}0.472{col 54}{space 4} -.037747{col 67}{space 3} .0814789
{txt}ad~8_itadopt {c |}{col 14}{res}{space 2}-.0510218{col 26}{space 2} .0368828{col 37}{space 1}   -1.38{col 46}{space 3}0.167{col 54}{space 4}-.1233106{col 67}{space 3} .0212671
{txt}a~20_itadopt {c |}{col 14}{res}{space 2} .0823934{col 26}{space 2} .0390996{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0057596{col 67}{space 3} .1590272
{txt}ad~1_itadopt {c |}{col 14}{res}{space 2}-.1004521{col 26}{space 2} .0617358{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4} -.221452{col 67}{space 3} .0205478
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m3c if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m3c if e(sample), residuals
{res}{txt}(962 missing values generated)

{com}. 
. gen sse_m3c = predsy_m3c * predsy_m3c if e(sample)
{txt}(962 missing values generated)

{com}. gen ssr_m3c = residsy_m3c * residsy_m3c if e(sample)
{txt}(962 missing values generated)

{com}. 
. egen sum_sse_m3c = total(sse_m3c) if e(sample)
{txt}(961 missing values generated)

{com}. egen sum_ssr_m3c = total(ssr_m3c) if e(sample)
{txt}(961 missing values generated)

{com}. 
. gen r2_m3c = sum_ssr_m3c/(sum_sse_m3c + sum_ssr_m3c)
{txt}(961 missing values generated)

{com}. 
. sum r2_m3c

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m3c {c |}{res}     11,590    .3981651           0   .3981651   .3981651
{txt}
{com}. 
. 
. * [MODEL C3: RELATIVE TYPE I ERROR RATE] FIGURE C2D: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:11,589}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .285656{col 26}{space 2} .0040284{col 37}{space 1}   70.91{col 46}{space 3}0.000{col 54}{space 4} .2777604{col 67}{space 3} .2935516
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2844582{col 26}{space 2} .0035495{col 37}{space 1}   80.14{col 46}{space 3}0.000{col 54}{space 4} .2775014{col 67}{space 3}  .291415
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .278874{col 26}{space 2} .0029825{col 37}{space 1}   93.50{col 46}{space 3}0.000{col 54}{space 4} .2730285{col 67}{space 3} .2847195
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2730184{col 26}{space 2} .0052834{col 37}{space 1}   51.67{col 46}{space 3}0.000{col 54}{space 4} .2626632{col 67}{space 3} .2833737
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2680146{col 26}{space 2} .0078723{col 37}{space 1}   34.05{col 46}{space 3}0.000{col 54}{space 4} .2525851{col 67}{space 3}  .283444
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .263788{col 26}{space 2} .0101308{col 37}{space 1}   26.04{col 46}{space 3}0.000{col 54}{space 4}  .243932{col 67}{space 3}  .283644
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .260264{col 26}{space 2} .0119952{col 37}{space 1}   21.70{col 46}{space 3}0.000{col 54}{space 4} .2367538{col 67}{space 3} .2837742
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2573682{col 26}{space 2} .0134811{col 37}{space 1}   19.09{col 46}{space 3}0.000{col 54}{space 4} .2309457{col 67}{space 3} .2837907
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2550258{col 26}{space 2} .0146237{col 37}{space 1}   17.44{col 46}{space 3}0.000{col 54}{space 4} .2263639{col 67}{space 3} .2836877
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2531624{col 26}{space 2} .0154652{col 37}{space 1}   16.37{col 46}{space 3}0.000{col 54}{space 4} .2228511{col 67}{space 3} .2834737
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2517034{col 26}{space 2} .0160513{col 37}{space 1}   15.68{col 46}{space 3}0.000{col 54}{space 4} .2202434{col 67}{space 3} .2831635
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2505743{col 26}{space 2} .0164293{col 37}{space 1}   15.25{col 46}{space 3}0.000{col 54}{space 4} .2183734{col 67}{space 3} .2827751
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE C2D{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Error Rate [MODEL C3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r+5)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2D.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2D.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2D.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. * [MODEL C3: RELATIVE TYPE I ERROR RATE] FIGURE C2E:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_catC==2) & LOW COMPLEXITY (relt1_interstate_catC==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. 
. margins r.relt1_interstate_catC if relt1_interstate_catC==0|relt1_interstate_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:5,907}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_catC@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     8.65{col 51}{space 2}   0.0033
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     8.95{col 51}{space 2}   0.0028
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     7.14{col 51}{space 2}   0.0075
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.80{col 51}{space 2}   0.0512
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.22{col 51}{space 2}   0.1359
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.52{col 51}{space 2}   0.2173
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.18{col 51}{space 2}   0.2769
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.01{col 51}{space 2}   0.3146
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.93{col 51}{space 2}   0.3351
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.90{col 51}{space 2}   0.3433
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.89{col 51}{space 2}   0.3444
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.90{col 51}{space 2}   0.3432
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_catC@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2}-.0281694{col 39}{space 2} .0095771{col 50}{space 5}-.0469402{col 64}{space 3}-.0093985
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2} -.027791{col 39}{space 2} .0092916{col 50}{space 5}-.0460021{col 64}{space 3}-.0095799
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2}-.0260913{col 39}{space 2} .0097627{col 50}{space 5}-.0452259{col 64}{space 3}-.0069568
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2}-.0244426{col 39}{space 2} .0125354{col 50}{space 5}-.0490116{col 64}{space 3} .0001264
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2}-.0231704{col 39}{space 2} .0155373{col 50}{space 5} -.053623{col 64}{space 3} .0072821
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2}-.0222222{col 39}{space 2} .0180138{col 50}{space 5}-.0575286{col 64}{space 3} .0130843
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2}-.0215451{col 39}{space 2} .0198145{col 50}{space 5}-.0603807{col 64}{space 3} .0172906
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2}-.0210865{col 39}{space 2}  .020968{col 50}{space 5}-.0621829{col 64}{space 3}   .02001
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2}-.0207936{col 39}{space 2} .0215707{col 50}{space 5}-.0630714{col 64}{space 3} .0214842
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2}-.0206139{col 39}{space 2} .0217544{col 50}{space 5}-.0632517{col 64}{space 3}  .022024
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2}-.0204945{col 39}{space 2} .0216747{col 50}{space 5}-.0629762{col 64}{space 3} .0219872
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2}-.0203828{col 39}{space 2} .0215039{col 50}{space 5}-.0625295{col 64}{space 3}  .021764
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C2E{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Relative Type I Error Rate [MODEL C3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2E.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2E.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2E.12-07-2024.gph} saved

{com}. *
. 
. 
. 
. 
. * [MODEL C3: RELATIVE TYPE I ERROR RATE] FIGURE C2F:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_catC==2) & LOW COMPLEXITY (relt1_diffoccupseek_catC==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. margins r.relt1_diffoccupseek_catC if relt1_diffoccupseek_catC==0|relt1_diffoccupseek_catC==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:5,824}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 29}{c TT}{hline 11}{hline 12}{hline 11}
{col 30}{text}{c |}         df{col 42}        chi2{col 54}     P>chi2
{res}{col 1}{text}{hline 29}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_catC@_at {c |}
{space 16}(2 vs 0)  1  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.23{col 54}{space 2}   0.6308
{txt}{space 16}(2 vs 0)  2  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.33{col 54}{space 2}   0.5670
{txt}{space 16}(2 vs 0)  3  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.67{col 54}{space 2}   0.4127
{txt}{space 16}(2 vs 0)  4  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.63{col 54}{space 2}   0.4288
{txt}{space 16}(2 vs 0)  5  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.50{col 54}{space 2}   0.4790
{txt}{space 16}(2 vs 0)  6  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.39{col 54}{space 2}   0.5314
{txt}{space 16}(2 vs 0)  7  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.30{col 54}{space 2}   0.5863
{txt}{space 16}(2 vs 0)  8  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.21{col 54}{space 2}   0.6477
{txt}{space 16}(2 vs 0)  9  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.13{col 54}{space 2}   0.7195
{txt}{space 16}(2 vs 0) 10  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.06{col 54}{space 2}   0.8057
{txt}{space 16}(2 vs 0) 11  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.01{col 54}{space 2}   0.9093
{txt}{space 16}(2 vs 0) 12  {res}{col 30}{text}{c |}{result}{space 2}        1{col 42}{space 3}     0.00{col 54}{space 2}   0.9695
{col 1}{text}                      Joint {col 30}{c |}{result}  (not testable)
{col 1}{text}{hline 29}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 30}{c |}{col 42} Delta-method
{col 30}{c |}   Contrast{col 42}   std. err.{col 54}     [95% con{col 67}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_catC@_at {c |}
{space 16}(2 vs 0)  1  {c |}{col 30}{res}{space 2}-.0040716{col 42}{space 2} .0084715{col 53}{space 5}-.0206754{col 67}{space 3} .0125322
{txt}{space 16}(2 vs 0)  2  {c |}{col 30}{res}{space 2}   -.0047{col 42}{space 2} .0082101{col 53}{space 5}-.0207915{col 67}{space 3} .0113916
{txt}{space 16}(2 vs 0)  3  {c |}{col 30}{res}{space 2} -.007375{col 42}{space 2} .0090027{col 53}{space 5}  -.02502{col 67}{space 3}   .01027
{txt}{space 16}(2 vs 0)  4  {c |}{col 30}{res}{space 2}-.0096096{col 42}{space 2} .0121447{col 53}{space 5}-.0334128{col 67}{space 3} .0141935
{txt}{space 16}(2 vs 0)  5  {c |}{col 30}{res}{space 2}-.0108603{col 42}{space 2} .0153402{col 53}{space 5}-.0409266{col 67}{space 3}  .019206
{txt}{space 16}(2 vs 0)  6  {c |}{col 30}{res}{space 2}-.0112119{col 42}{space 2} .0179122{col 53}{space 5}-.0463192{col 67}{space 3} .0238955
{txt}{space 16}(2 vs 0)  7  {c |}{col 30}{res}{space 2} -.010749{col 42}{space 2} .0197536{col 53}{space 5}-.0494654{col 67}{space 3} .0279675
{txt}{space 16}(2 vs 0)  8  {c |}{col 30}{res}{space 2}-.0095565{col 42}{space 2} .0209116{col 53}{space 5}-.0505424{col 67}{space 3} .0314295
{txt}{space 16}(2 vs 0)  9  {c |}{col 30}{res}{space 2}-.0077191{col 42}{space 2} .0214916{col 53}{space 5}-.0498418{col 67}{space 3} .0344035
{txt}{space 16}(2 vs 0) 10  {c |}{col 30}{res}{space 2}-.0053218{col 42}{space 2} .0216324{col 53}{space 5}-.0477204{col 67}{space 3} .0370769
{txt}{space 16}(2 vs 0) 11  {c |}{col 30}{res}{space 2}-.0024491{col 42}{space 2} .0214988{col 53}{space 5} -.044586{col 67}{space 3} .0396877
{txt}{space 16}(2 vs 0) 12  {c |}{col 30}{res}{space 2}  .000814{col 42}{space 2} .0212768{col 53}{space 5}-.0408877{col 67}{space 3} .0425157
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE C2F{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Relative Type I Error Rate [MODEL C3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2F.12-07-2024.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2F.12-07-2024.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model C3.FIGURE C2F.12-07-2024.gph} saved

{com}. 
. 
. 
. 
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX C MODELS.12-07-2024.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 8 Dec 2024, 18:01:06
{txt}{.-}
{smcl}
{txt}{sf}{ul off}